Variable Rate Technology Research Articles

Trends of Soil and Solution Nutrient Sensing for Open Field and Hydroponic Cultivation in Facilitated Smart Agriculture

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system.

The Application of Climate Smart Agriculture Technology to Optimise Fertiliser Usage at Paddy Field Brunei Darussalam

Abstract Global food insecurity has already been rising, mainly due to climate change phenomena. This is aggravated by supply chain disruptions due to the war in Ukraine and economic fallout of the COVID-19 pushing food prices to an all time high. The great challenge is to adapt to climate change while trying to increase food production. Climate Smart Agriculture Technology (CSAT) is a set of agricultural practices and technologies that can boost productivity, enhance resilience and reduce GHG emissions. Central to the success of CSAT is the application of precision farming with the use of IOT sensors and aerial photography. This research examines the application of CSAT in a paddy field in Brunei Darussalam. Data from soil sensors and aerial drone imagery were used to influence better and more informed decision-making. Variable rate technology (VRT) was used to control the fertilizer application based on what was needed by the crop. Since a precise amount of fertiliser was applied, VRT can boost the efficiency of resource utilization, leading to increased crop yields and profitability. The application of CSAT in this case study saved the farmer around 60% by changing the type of fertilizer and reducing the amount used.

A New Proposal for Soybean Plant Stand: Variation Based on the Law of the Minimum.

The hypothesis of this study is that it is possible to determine the plant stand in the soybean (Glycine max L. Merril) crop based on the spatial variability of management units, which are limiting factors in maximizing crop yield. Our objectives were as follows: (I) to evaluate the relationship between soil physical and chemical attributes to establish potential management units for variable-rate seeding; (II) to propose a method for varying plant stands based on the law of minimum soil nutrients; an (III) to relate the interaction between different plant stands on soybean grain yield, taking into account the interaction between the spatial variability of the mapped attributes. Field experiments were carried out on two plots over two agricultural years. The areas were seeded by randomly varying the soybean stand across strips in the first year. The most limiting soil nutrient was established and used, together with the soil CEC, to determine management units (MUs), which were also used to seed soybeans in VRT (Variable Rate Technology) in the same plots in the second year. MUs with the lowest restriction for maximizing yield were sown in the second year with the lowest plant stand. Data were processed using multivariate statistics. Our findings reveal that it is possible to establish MUs for seeding soybeans with different stands following the spatial variability of limiting soil nutrients according to the law of the minimum and thus increase the crop grain yield. Spatial variability of potassium (K) in the plot, identified as limiting, affected the spatial variability of grain yield. Decreasing plant stands in MUs with the lowest limitation level increases yield. However, increasing the stand in MUs with a higher limitation level can lead to increased intraspecific competition, affecting yield as well as increasing input costs.

Examining the adaptability of soil pH to soil dynamics using different methodologies: A concise review

Soil pH is crucial to soil health, influencing nutrient availability, microbial activity, and plant growth. This review aims to assess the adaptability of soil pH under changing soil conditions by analyzing natural and human factors. Information was gathered from various sources, including peer-reviewed articles, field studies, and recent advances in soil science. The study explores how natural factors such as parent material, climate, and vegetation establish baseline soil pH, while human activities such as intensive farming and land-use changes further modify it, often leading to soil acidification or alkalinization. Traditional management methods like lime application, organic amendments, and crop rotation are reviewed for their effectiveness in stabilizing soil pH and their limitations under varying soil conditions. The review also explores modern technological innovations like precision agriculture, which uses soil sensors and variable rate technology for targeted pH management, and biological approaches, such as microbial inoculants, to enhance nutrient cycling and organic matter decomposition. Integrating these traditional and contemporary approaches is essential for sustainable soil pH management and long-term productivity. The findings highlight the need for a holistic approach that combines historical knowledge with emerging technologies to promote sustainable agricultural practices and environmental conservation.

Real-time nitrogen monitoring and management to augment N use efficiency and ecosystem sustainability–A review

Agriculture faces the pressing challenge of feeding a growing population while preserving the environment and natural resources. Nitrogen (N) is an essential nutrient for plant growth, and its availability in soil is a key indicator of fertility. However, the indiscriminate use of N fertilizers can lead to nutrient imbalances and soil degradation, underscoring the need for accurate and efficient soil N management. Traditional sampling and analysis methods are time-consuming and prone to error, making real-time soil N assessments crucial for effective management. Moreover, precise estimation of soil N is vital for monitoring losses, developing targeted fertilizer strategies, and enhancing crop productivity and N use efficiency. Real-time N management, which involves applying N as needed during critical growth stages, can significantly improve its usage efficiency. To achieve this, the leaf color chart offers a simple, inexpensive, and user-friendly solution for assessing N needs based on leaf color, facilitating real-time management. Furthermore, sensors and the Internet of Things (IoT) play a key role in sustainable soil management and crop productivity, contributing to the development of resilient food systems and reducing uncertainty in global food markets. Accurate, rapid, cost-effective methods for assessing soil N levels are essential to achieve these sustainable goals. This review delves into the current status, limitations, and future of N-sensing in precision agriculture, highlighting cutting-edge technologies such as real-time monitoring using remote and proximal sensors, ground-based canopy sensors, drones, and Unmanned Aerial Vehicle (UAV) equipped with high-resolution cameras or multispectral/hyperspectral sensors, Geographic Information Systems (GIS)- Global Positioning Systems (GPS) integration (GIS-GPS), data analysis, Variable Rate Technology (VRT) and crop models for precise N management. By harnessing these innovations, we can revolutionize agriculture, benefiting plant health and promoting a more sustainable future.

Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review

Robotics and variable rate technology (VRT) have shown great potential for site-specific weed management (SSWM), but these technologies face several challenges like accurate weed identification, high initial cost, integration with existing farming practices, and the need for the development of precise and robust system for real-time application. Given the lack of comprehensive information on the current state of robotic technologies for weed management, this review study aims to provide an extensive overview of the individual components associated with SSWM technologies, such as autonomous navigation, imaging sensors, and weeding approaches. To perform this study, 91 technical papers were selected that involve the status, challenges, and potential improvements of individual components integrated to develop an efficient weed management technology. The key outcomes can be summarized as: (a) real-time kinematic-enabled global positioning system (RTK-GPS) and machine vision guidance systems are widely adopted technology for automatic row guidance because of their low cost, high accuracy, and easy accessibility, (b) GPS technology face challenges due to signal interference and dependency on satellite availability for accurate positioning, (c) sensor fusion has been suggested as a potential solution to improve the accuracy of positioning and weed identification, (d) deep learning techniques for weed identification offer significant promise in enhancing the feasibility of implementing robotic weed control in real-time, (e) due to robust nature of hyperspectral imaging, research in weed identification for real-time in open-field conditions is still lagging, (f) plethora of university-based research studies are limited to model optimization and simulation studies, and (g) integrated weed management practices, such as a combined application of mechanical and laser weeding, could be the future of robotic weed control. This review will serve as a valuable reference to extension educators, growers, researchers, and stakeholders for adopting state-of-the-art and sustainable practices in SSWM.

Revolutionizing Agriculture: Innovative Techniques, Applications, and Future Prospects in Precision Farming

Precision agriculture (PA) represents a transformative approach to farming, employing advanced technologies to enhance productivity, efficiency, and sustainability. This review article provides an in depth analysis of the latest innovations in PA techniques, their diverse applications, and future directions. Precision agriculture is revolutionizing the agricultural landscape by integrating sophisticated tools such as GPS, remote sensing, Internet of Things (IoT), and big data analytics. These technologies enable farmers to monitor and manage variability in crop production meticulously, optimize the use of inputs, and enhance overall farm management practices. The key innovations in PA include the development and application of Geographic Information Systems (GIS) and Global Positioning Systems (GPS), which facilitate accurate mapping and variable rate technology (VRT) for site specific input management. Remote sensing technologies, encompassing both satellite imagery and UAVs (unmanned aerial vehicles), provide critical insights into crop health, soil conditions, and weather patterns, allowing for proactive and informed decision making. The integration of IoT in agriculture involves deploying sensors and connected devices to monitor soil moisture, temperature, and other environmental parameters in real time. This integration supports precision irrigation, climate monitoring, and efficient resource utilization. Big data analytics further enhances PA by processing vast amounts of data to generate actionable insights, enabling predictive analytics and decision support systems (DSS) that aid in optimizing farming operations. The article explores the applications of these advanced techniques in crop management, resource use optimization, and environmental stewardship. Examples include variable rate application of fertilizers, precision irrigation systems, and automated machinery such as drones and robotic harvesters. These innovations lead to significant improvements in crop yields, resource efficiency, and sustainability. Moreover, the review addresses the challenges associated with the adoption and implementation of PA technologies. These challenges include data management complexities, high initial costs, limited accessibility, and the need for technical expertise. The article discusses potential solutions such as cloud computing, machine learning algorithms, government subsidies, collaborative models, and comprehensive training programs to mitigate these barriers, the review highlights the integration of advanced technologies like artificial intelligence (AI), blockchain, and enhanced connectivity through 5G networks as pivotal developments that will further revolutionize precision agriculture. AI and machine learning will enhance predictive modeling and automated decision making, while blockchain will ensure transparency and traceability in supply chains. Enhanced connectivity will facilitate real time monitoring and collaborative platforms, driving efficiency and innovation in farming practices.

Precision agriculture: Leveraging data science for sustainable farming

Precision agriculture leverages data science and technology to optimize farming practices, enhance crop yields, and promote sustainability. This review examines how data-driven approaches are revolutionizing agriculture through improved decision-making, resource efficiency, and environmental stewardship. Key technologies enabling precision agriculture are discussed, including remote sensing, IoT sensors, AI/machine learning, and farm management information systems. The paper explores applications such as variable rate technology, yield mapping, and predictive analytics. Challenges related to data management, interoperability, and adoption barriers are analyzed. The review concludes that precision agriculture, powered by data science, offers significant potential to address food security and sustainability challenges, but requires continued research and interdisciplinary collaboration to realize its full benefits.

Broad Scope of Site‐Specific Crop Management and Specific Role of Remote Sensing Technologies Within It—A Review

ABSTRACTPrecision agriculture (PA) has great potential to increase agricultural productivity and profitability while reducing input costs and environmental impacts. Within PA, site‐specific crop management (SSCM) is considered the main premise, in which tillage operations and precise crop inputs (such as seed, fertiliser, water, pesticide and agrochemical) are applied according to field variability. The main aim of this review was to highlight the methods and tools used for spatial crop monitoring, soil and weather data influencing crop productivity and to support the adoption of SSCM technology. To achieve this goal: we discussed the main five components of SSCM, methods for monitoring crop and soil data, delineating field management zones (FMZs) and variable rate technologies (VRT) such as precision planting and digital smart sensors used for SSCM application. The review summarised that recent advances in plant and soil sensing systems, artificial intelligence (AI) and machine learning should be used in retrieving and analysing GIS big data for optimised crop inputs supply. Within VRT, light‐bar systems, automatic controllers and sensors are user‐friendly technologies that should be employed in SSCM solution. The authors highlight that adoption of PA can be increased through proper training and education of the farmers, and developing simple, affordable and efficient PA technologies. The review suggests five criteria that should be strictly adopted to get maximum benefits from SSCM: (i) all factors influencing crop yields can be identified; (ii) their effects on crop yields can be determined by using appropriate digital tools and crop modelling; (iii) variable rate crop inputs (VRCIs) should be calculated based on accurate information obtained from plant, soil and environment; (iv) targeted crop inputs should be exercised through global positioning system (GPS) enabled automatic controllers or wireless sensors network (WSN); and (v) right doses of crop inputs (e.g., nitrogen and irrigation) must be applied at the right time and place.

Site Specific Nutrient Management (SSNM): Principles, Key Features and its Potential Role in Soil, Crop Ecosystem and Climate Resilience Farming

As the world population expands, farming practices have intensified to meet increased food demand. Modern crop varieties now require higher levels of fertilization compared to traditional organic methods, which are insufficient to meet these new demands. However, inefficient nutrient management and imbalances in field nutrient levels have led to various crop issues. To achieve higher agricultural output and productivity, there has been a notable increase in the application of chemical fertilizers, which unfortunately contributes to soil and water pollution, degradation of soil fertility, and nutrient imbalances. In response, modern technologies are being leveraged to promote sustainable food production and foster balanced agricultural development. Site specific nutrient management (SSNM) aims to empower farmers to adjust fertilizer application dynamically to meet the nutrient requirements of high-yielding crops, bridging the gap between natural nutrient sources like soil, crop residues, manure, and irrigation water. It is based on the principles of 4Rs: right product, right dose, right time and right place. Modern technologies like Optical sensors, Crop Manager, Remote Sensing, Nutrient Expert®, fertility mapping, Active Canopy Sensors, Variable rate technologies etc. are used to enhance the potential of SSNM to higher level. The focus is on optimizing nutrient application timing and rates to maximize crop yield and nutrient efficiency, thereby enhancing the economic value of harvest per unit of fertilizer used, rather than simply increasing or decreasing fertilizer use.

Optimising Precision Agriculture Choices for Arable Farmers in Germany and the UK: the LINKDAPA Approach

SummaryFarmer adoption of so‐called Precision Agriculture (PA) or ‘smart’ technologies in the arable sector has grown in the last few decades with a focus on Global Navigation Satellite Systems (GNSS) and variable rate technologies (VRT). This has led to increased generation of large volumes of data about fields and their crop yields which could be used to increase the environmental and financial performance for farmers. However, survey results show that cost and adaptability have been issues for many farmers in the UK and Germany that have held back such adoption. The LINKDAPA (LINKing multi‐source Data for Adoption of Precision Agriculture) project's approach sought to minimise both concerns by creating a customisable web platform that incorporates both GNSS and VRT into one, easy to use, affordable option for farmers. The project developed an online cloud‐based decision support tool which takes into account different fertiliser strategies based on novel algorithms using soil, historic yield and satellite data. Co‐created by researchers, farmers and agricultural technology firms, the LINKDAPA approach offers both economical and easy to implement solutions for farm management to mitigate resource loss‐ratios such as in fertiliser use, provide financial performance analyses, and multi‐year graphical imagery for soil mapping.

Remote Sensing and Geographic Information Systems for Precision Agriculture: A Review

Precision agriculture aims to optimize crop production and minimise environmental impacts by using information technology, remote sensing, satellite positioning systems, and proximal data gathering. This review paper examines current applications and future directions of remote sensing and geographic information systems (GIS) for precision agriculture. Remote sensing provides data on crop health, soil conditions, water status, and yield which can guide variable rate applications within fields. Satellite and aerial platforms allow multispectral and hyperspectral imaging for vegetation indices analysis, crop classification, and stress detection. GIS technology integrates these data layers to model and map variations, develop prescription maps, and analyse spatial relationships. Key research frontiers include high-resolution satellite and drone data for within-field analysis, better integration of proximal and remote sensing, online nutrient and yield monitors, real-time prescription modelling, and predictive analytics using machine learning. Adoption continues to increase with better data analytics tools and greater economic returns realized. Remote sensing and GIS provide an integral platform for variable rate technologies, predictive modelling, and data-driven decision-making for precision agriculture.

Enhancing Sustainable Crop Production through Innovations in Precision Agriculture Technologies

Precision agriculture technologies provide innovative tools to optimize crop production while minimizing environmental impacts. This review examines recent advances in precision ag systems to enhance sustainable agriculture. Key innovations include: remote and proximal crop sensing techniques leveraging hyperspectral imaging, thermal imaging, and Lidar to assess crop health and stress status; variable rate technologies like targeted sprayers and precision planters to reduce input waste; data analytics and decision support systems that integrate multi-source data streams to guide site-specific intervention; robotics and automation for precision field operations; and advanced breeding techniques and genomic tools enabling development of stress resilient, high yielding varieties. Adoption barriers, future technology trajectories, and priority research needs are discussed to further advance precision solutions that support productivity, efficiency, and sustainability goals.

Application of Precision Agriculture Technologies for Sustainable Crop Production and Environmental Sustainability: A Systematic Review.

Precision agriculture technologies (PATs) transform crop production by enabling more sustainable and efficient agricultural practices. These technologies utilize data-driven approaches to optimize the management of crops, soil, and resources, thus enhancing both productivity and environmental sustainability. This article reviewed the application of PATs for sustainable crop production and environmental sustainability around the globe. Key components of PAT include remote sensing, GPS-guided equipment, variable rate technology (VRT), and Internet of Things (IoT) devices. Remote sensing and drones deliver high-resolution imagery and data, enabling precise monitoring of crop health, soil conditions, and pest activity. GPS-guided machinery ensures accurate planting, fertilizing, and harvesting, which reduces waste and enhances efficiency. VRT optimizes resource use by allowing farmers to apply inputs such as water, fertilizers, and pesticides at varying rates across a field based on real-time data and specific crop requirements. This reduces over-application and minimizes environmental impact, such as nutrient runoff and greenhouse gas emissions. IoT devices and sensors provide continuous monitoring of environmental conditions and crop status, enabling timely and informed decision-making. The application of PAT contributes significantly to environmental sustainability by promoting practices that conserve water, reduce chemical usage, and enhance soil health. By enhancing the precision of agricultural operations, these technologies reduce the environmental impact of farming, while simultaneously boosting crop yields and profitability. As the global demand for food increases, precision agriculture offers a promising pathway to achieving sustainable crop production and ensuring long-term environmental health.

Precise application of water and fertilizer to crops: challenges and opportunities.

Precision water and fertilizer application technologies have emerged as crucial innovations in sustainable agriculture, addressing the pressing need to enhance crop yield and quality while optimizing resource use and minimizing environmental impacts. This review systematically explores the latest advancements in precision water and fertilizer application technologies. It examines the integration of advanced sensors, remote sensing, and machine learning algorithms in precision agriculture, assessing their roles in optimizing irrigation and nutrient management. The study evaluates various precision techniques, including micro-irrigation systems, variable rate technology (VRT), and predictive modeling, along with their implementation in diverse agricultural settings. Furthermore, the review addresses the challenges posed by soil environmental heterogeneity and emphasizes the necessity for a scientific index system to guide precise applications. Advanced irrigation methods, such as subsurface drip irrigation and micro-sprinkling, improve water-use efficiency and reduce salinity levels, while precision fertilization techniques optimize nutrient uptake and minimize leaching. The integration of machine learning and remote sensing facilitates real-time monitoring and adaptive management, resulting in increased resource use efficiency and reduced environmental pollution. However, the effectiveness of these technologies is contingent upon addressing soil heterogeneity and developing standardized application indices. This review highlights the novel combination of advanced sensing technologies and data analytics in precision agriculture, enabling targeted interventions tailored to specific field conditions. It underscores the importance of integrating soil microbial community dynamics and biochemical indicators with precision management practices to enhance soil fertility and crop performance. Furthermore, the development of predictive models and time series analysis tools represents a significant advancement in anticipating and responding to changing environmental conditions. Precision water and fertilizer application technologies offer substantial benefits for sustainable agricultural practices by improving crop yields, enhancing resource efficiency, and mitigating environmental impacts. The strategic integration of these technologies with tailored agricultural practices and robust monitoring systems is essential for optimizing nutrient cycling and maintaining soil health. Addressing existing challenges through interdisciplinary research and collaborative efforts will further advance the implementation of precision agriculture, contributing to long-term soil sustainability and global food security.

Open-Format Prescription Maps for Variable Rate Spraying in Orchard Farming

Highlights Straightforward procedure to code prescription maps independent of manufacturers. Methodology suitable for orchard crops and vineyards including pressures up to 10 bar and flowrates up to 100 L/min. Algorithm validated for both trellis-based continuous rows and traditional orchard rows of discrete trees. Method scalable to other agricultural operations, such as fertilizing or data registration for auditing purposes. Abstract. The implementation of crop protection solutions contributing to significant reductions in the use and risk of chemical pesticides has motivated the interest of site-specific systems, such as variable rate technologies based on user-set prescriptions. However, the practical implementation of variable rates has been dissuasive for many growers who are not familiar with digital solutions, as proprietary firmware is often difficult to grasp. This paper presents a method for formatting prescription maps that is independent of manufacturers, can be easily and freely implemented by industry and can be adopted by growers not familiar with advanced software tools. The methodology was successfully demonstrated for perennial specialty crops, in particular for trellised vineyards in vertical shoot position and for traditional olive groves. Results showed that the variations within user prescription maps were translated to the fields, but flow errors appeared due to the challenge of measuring flow and pressure in the highly pulsating actuation of pulse-width-modulated (PWM) valves. Such errors diminished with the use of low-volume rates and the optimal selection of the nozzles when adjusted to the real pressure measured in the field. The use cases validated in the field showed the consistency of the method for highly populated prescription maps of 1,221 points/ha accessible to advanced farmers, as well as for modest maps of 80 points/ha within reach of the majority of growers. Keywords: Automated map building, Local tangent plane coordinates, Orchard crop operations, Precision farming, Prescription maps, User-centered approach, Variable rate spraying.

Comprehensive study of on-the-go sensing and variable rate application of liquid nitrogenous fertilizer

Variable rate technology (VRT) provides a sustainable, efficient, and cost-effective nutrient application option. A pioneered study is carried out to develop a variable rate liquid fertilizer application system to detect on-the-go deficiency of Nitrogen within a field and apply it according to the requirement of crop. The system includes microcontroller, nitrogen sensor (i.e. Green-seeker sensor), proportional solenoid valve, pressure relief valve, pump, battery, etc. Microcontroller receives a signal from Green-seeker sensor and it transmits to the proportional solenoid valve (PSV). The PSV opening is change based on the prescribed nutritional dose. The experimental study is conducted on groundnut crop. The experiment is carried out for different level of fertilizer (N1-150, N2– 200, N3-250, and N4-300 l ha−1) as well as different operating speed is 3, 4, 5 km h−1 to estimate actual fertilizer dropped (AFD), % error, droplet size, droplet density (DD), uniformity coefficient (UC), and spray deposition. The response time of the system is in the range of 1.12 s to 1.80 s. It is observed from the experimental study that the error in the fertilizer to be dropped and AFD increases with increase in the speed of operation. The percentage error is found to be 2.73 %, which is the lowest one, at 3 km h−1 operating speed. The maximum droplet size is found to be 272.5 μm at 5 km h−1 forward speed and 300 l ha−1 nitrogen application rate, whereas minimum droplet size is found to be 241.23 μm at 3 km h−1 forward speed and 150 l ha−1 nitrogen application rate. The present study proposes maximum of 24.30 % saving in the fertilizer application for groundnut crop. Thus, the present system improved fertilizer use efficiency, profitability while minimizing environmental hazards.

Performance Analysis of Relative GPS Positioning for Low-Cost Receiver-Equipped Agricultural Rovers.

Global navigation satellite systems (GNSSs) became an integral part of all aspects of our lives, whether for positioning, navigation, or timing services. These systems are central to a range of applications including road, aviation, maritime, and location-based services, agriculture, and surveying. The Global Positioning System (GPS) Standard Position Service (SPS) provides position accuracy up to 10 m. However, some modern-day applications, such as precision agriculture (PA), smart farms, and Agriculture 4.0, have demanded navigation technologies able to provide more accurate positioning at a low cost, especially for vehicle guidance and variable rate technology purposes. The Society of Automotive Engineers (SAE), for instance, through its standard J2945 defines a maximum of 1.5 m of horizontal positioning error at 68% probability (1σ), aiming at terrestrial vehicle-to-vehicle (V2V) applications. GPS position accuracy may be improved by addressing the common-mode errors contained in its observables, and relative GNSS (RGNSS) is a well-known technique for overcoming this issue. This paper builds upon previous research conducted by the authors and investigates the sensitivity of the position estimation accuracy of low-cost receiver-equipped agricultural rovers as a function of two degradation factors that RGNSS is susceptible to: communication failures and baseline distances between GPS receivers. The extended Kalman filter (EKF) approach is used for position estimation, based on which we show that it is possible to achieve 1.5 m horizontal accuracy at 68% probability (1σ) for communication failures up to 3000 s and baseline separation of around 1500 km. Experimental data from the Brazilian Network for Continuous Monitoring of GNSS (RBMC) and a moving agricultural rover equipped with a low-cost GPS receiver are used to validate the analysis.

Precision nitrogen management in rainfed durum wheat cultivation: exploring synergies and trade-offs via energy analysis, life cycle assessment, and monetization

Fertilization with variable rate technology (VRT) is a pivotal technique of precision agriculture proposed for eco-friendly farming practices. Yet the magnitude of environmental benefits is often not well known or is highly variable. This study used a multi-indicator model and life cycle-based indicators to compare the performance of rain-fed durum wheat production using uniform (UA) and variable N fertilization (VRT). Two functional units were used: 1 ha of cultivated wheat and 1 ton of wheat produced. The energy analysis indicated that VRT increases energy use efficiency and productivity by 13.3%, reduces specific energy and total energy input by 11.7%, and increases net energy gain by 15.3%. The life cycle assessment (LCA) analysis indicated that for some environmental impacts, VRT had minor negative effects due to the comparable yield performance with UA. Yet, the VRT had a noteworthy positive impact on global warming, fine particulate matter formation, stratospheric ozone depletion, terrestrial acidification, and marine eutrophication, generating a final environmental benefit of 12.2% for 1 ton of product and 13.3% for 1 ha of land. Economic valuation or monetization of LCA results using monetization weighting factors indicated indirect economic benefits of VRT can be up to 6.6% for 1 ton of product and 7.7% for 1 ha of land. Our findings support the use of nitrogen fertilization with VRT for sustainable extensification and improved eco-efficiency of wheat production in a Mediterranean context. As a result of our research, we conclude that future case studies on annual crops with moderate land requirements should employ multiple metrics and functional units, as well as the concepts of monetization and life cycle assessment, to investigate trade-offs between yield, economic, and environmental benefits and to aid decision-making about the true sustainability of proposed farming technologies.Graphical abstract

Global Navigation Satellite Systems as State-of-the-Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science

Global Navigation Satellite Systems (GNSS) in precision agriculture (PA) represent a cornerstone for field mapping, machinery guidance, and variable rate technology. However, recent improvements in GNSS components (GPS, GLONASS, Galileo, and BeiDou) and novel remote sensing and computer processing-based solutions in PA have not been comprehensively analyzed in scientific reviews. Therefore, this study aims to explore novelties in GNSS components with an interest in PA based on the analysis of scientific papers indexed in the Web of Science Core Collection (WoSCC). The novel solutions in PA using GNSS were determined and ranked based on the citation topic micro criteria in the WoSCC. The most represented citation topics micro based on remote sensing were “NDVI”, “LiDAR”, “Harvesting robot”, and “Unmanned aerial vehicles” while the computer processing-based novelties included “Geostatistics”, “Precise point positioning”, “Simultaneous localization and mapping”, “Internet of things”, and “Deep learning”. Precise point positioning, simultaneous localization and mapping, and geostatistics were the topics that most directly relied on GNSS in 93.6%, 60.0%, and 44.7% of the studies indexed in the WoSCC, respectively. Meanwhile, harvesting robot research has grown rapidly in the past few years and includes several state-of-the-art sensors, which can be expected to improve further in the near future.