Context and objective Understanding the response of cereal yields to fertilizer nitrogen (N) is crucial in modelling food security within planetary boundaries, and in economic optimization of N use within environmental limits. Here we explore how agronomic N efficiency (N-AE, kg grain per kg N applied) in cereals depends on locally attainable yield (Ymax, highest yield observed per trial) and control yield (Y0, yield at zero fertilizer-N). Methods Data were collected from 593 N response trials with winter wheat and spring wheat (Triticum aestivum L.), spring barley (Hordeum vulgare L.), and spring oats (Avena sativa L.) in Denmark, the Netherlands, Spain, the UK and Sweden, giving 3268 N-AE data points. We applied linear mixed effect modelling to quantify the effects of N rate (NF), Ymax, Y0, and the ‘yield window’ ΔY (the difference Ymax-Y0) on N-AE. Results and Conclusions N-AE was largely governed by the yield window, besides the well-known effect of N rate. As such, the yield window captured effects of soil, year, cultivar and country on N-AE. Combined, ΔY and NF explained 80% (barley), 86% (winter wheat), 94% (spring wheat), and 91% (oats) of N-AE variance. Depending on crop species, a widening of ΔY by 1 t/ha raised N-AE by 4.7–6.5 kg grain per kg N, at N rates common in local practice. Substantial differences in mean N-AE at given N rate were found between data subsets by country. Economically optimal N rate (Nopt) varied widely between subsets, with averages of 175 kg N/ha (UK trials) and 215 kg N/ha (Swedish trials) at N-to-grain price ratio P = 3; or 156 and 200 kg/ha, respectively, at P = 6. Across trials, N-AE at Nopt responded linearly to ΔY by 5.0 (spring barley) and 3.2 (winter wheat) kg/kg per 1 t/ha increment of ΔY, independent of price ratio P. Significance The new concept of yield window provides opportunities for N optimization in modelling and practice. A larger yield window reduces N requirement per unit grain output well beyond reductions commonly associated with changes in yield potential in food security assessments. The newly presented N-AE model enables better estimation of N requirements to meet cereal production targets. It also enables a generic expression for optimal N rate as a function of yield window. Provided that control yield can be estimated, the model may contribute to more sustainable N management in cereals.
Climate change poses significant threats to European agricultural production, with increasing frequency and severity of adverse weather events impacting crop yields. Agroforestry, the integration of woody elements into agricultural systems, is recognized as a vital agroecological approach for both climate change mitigation and adaptation, yet key mechanistic and long-term performance questions remain unresolved. This study utilized the mechanistic Hi-sAFe model to simulate 100 years (2001–2100) of silvoarable agroforestry performance at Wakelyns farm in southeastern England, focusing on winter wheat (Triticum aestivum) and pea (Pisum sativum) yields under intermediate (Representative Concentration Pathway 4.5) and very high (Representative Concentration Pathway 8.5) emissions scenarios. The research assessed yield stability, underlying microclimatic and phenological mechanisms, and long-term land-use efficiency (land equivalent ratio). This study demonstrates for the first time that agroforestry functions as a climate shock absorber by protecting crops during a critical early-season phenological window, preventing catastrophic yield failures under climate change scenarios. These protective effects were mechanistically linked to microclimatic modification, likely shade, provided by newly emerged walnut (Juglans regia) leaves during the early stages of crop flowering and grain filling, rather than during peak summer heat. While overall yield stability assessed statistically was not significantly enhanced, the mitigation of extreme downside risk represents a profound benefit for farm resilience. Analysis of land equivalent ratio revealed a substantial initial productivity lag, with consistent land equivalent ratio > 1 achieved only after 80 years for wheat and 40 years for pea, highlighting economic adoption barriers but also the potential for optimized system design and adaptive management to accelerate productivity gains. Overall, these results identify a previously unreported mechanistic and temporal basis for agroforestry’s capacity to buffer temperate arable crops against climate-induced yield shocks.
This study focuses on the application of the ECOSSE-model to estimate greenhouse gas (GHG) emissions (CO2, N2O, and CH4) from an intensively managed grazed grassland located in Easter Bush, south-east Scotland (55° 52′ N, 03° 02′ W). The field has been under continuous permanent grassland management for about 20 years with a species composition of perennial ryegrass and white clover. The soil at the site is classified as mineral soil (i.e., sandy clay loam) with a pH of 5.1 and a clay fraction of 20–26
Crop protection and pest management are major economic and environmental concerns throughout Europe. The consultation of decision support systems (DSS) to guide decisions relating to Integrated Pest Management (IPM) is one of the key principles of IPM, reducing the ambiguity around potential risks to crop health. ‘Pests’ in this context include invertebrate pests, weeds and pathogens. The impact of DSS can be limited by a lack of awareness of DSS availability, inconsistencies in the user functions of different DSS, regional fragmentation of access, and a lack of transparency of the origin, validity, and benefits of DSS. Failure to address these limitations undermines trust in IPM DSS and leads to a reluctance of farmers and advisors to invest time in consulting multiple DSS sources as part of their agronomic decision toolbox. The EU-funded IPM Decisions project (Grant agreement ID: 817617) addressed these limitations by creating a Europe-wide free-access online platform. The IPM Decisions platform was designed in consultation with farmers, advisors and wider stakeholders to increase access to and uptake of IPM DSS integrated within it. It offers an end-point for IPM researchers and DSS developers to make adapted and novel DSS available to users, and provides a ‘one-stop shop' for farmers and advisors looking to consult free access or paid IPM DSS. Dedicated dashboards within the platform facilitate farm set up, consultation of DSS, comparison of DSS outputs, and adjustment of model parameters for adaption to different pests/regions. The IPM Decisions digital infrastructure enables easy integration of models and data with external platforms, providing a framework for accessing and sharing models and data between researchers and developers. The platform therefore provides both a ready to go user interface for new DSS, as well as the infrastructure to support and connect existing and future user interfaces.
The safety of school-going children during transportation is one of the most crucial concerns for parents, school administrators, and government authorities. Traditional transportation systems often lack real-time monitoring, incident detection, and immediate communication capabilities. This review paper focuses on analyzing the design and development of Smart School Bus Monitoring Systems that integrate Internet of Things (IoT), GPS, GSM, and sensor-based technologies. By reviewing recent works and methodologies, this paper highlights innovations in accident detection, fire sensing, and emergency alert mechanisms that enhance the overall safety and reliability of school transportation. Future improvements involving Artificial Intelligence (AI), facial recognition, and cloud-based analytics are also discussed to suggest pathways for the next generation of smart school transport systems.
