Field Trial Data Research Articles

Optimization of Casing Design for Complex-Structure Wells

This study aims to address the technical challenges encountered during drilling and completion of complex-structure wells, improving the efficiency and safety of casing design. Complex-structure wells, such as horizontal wells, multilateral wells, and extended-reach wells, impose higher demands on casing design due to their intricate geological conditions. Traditional design methods, often reliant on empirical formulas and simplified assumptions, struggle to meet the evolving needs of modern drilling technology. Therefore, this research is dedicated to developing a novel optimization method for casing design in complex-structure wells to enhance drilling efficiency and economic benefits. The study employs a combination of numerical simulation and optimization algorithms. Initially, a comprehensive mathematical model is established, factoring in formation properties, drilling fluid performance, and borehole stability, to forecast drilling performance under various casing designs. Genetic algorithms are then utilized to optimize casing parameters, seeking the optimal casing design that meets safety and economic constraints. The effectiveness of the optimized solution is validated through field trial data and compared against conventional design methods. It is found that the optimized casing design significantly reduces drilling risks, accelerates drilling speed, and cuts drilling costs. Specifically, under the premise of maintaining borehole stability, the optimized scheme decreases drilling time by an average of 15% and reduces drilling costs by approximately 10%. Moreover, the proposed method exhibits strong adaptability, capable of being flexibly adjusted according to different well types and geological conditions, providing a robust technical foundation for efficient drilling of complex-structure wells.

Evaluation of Autonomous Navigation and Path Accuracy for Unmanned Surface Vehicles Using IMU Sensor and GPS Data

The development of autonomous systems for environmental monitoring is becoming increasingly important. This project focuses on creating a robotic marine vessel, Aqua Sense, for efficient water quality monitoring. Existing methods are often labour-intensive, time-consuming, and lack real-time data capability. The objective is to develop a robotic vessel with autonomous navigation for water quality applications. A comprehensive design approach, including a literature review and careful component selection, was employed. Key components include the Arduino Mega, Blue Robotics T200 thruster, GlobalSat BU-353N5 GPS sensor, BNO055 Inertial Measurement Unit (IMU), and a Li-po 11.1v battery. In this paper, several field trials data collection is presented to evaluate the navigation algorithm. These findings validate the feasibility of Aqua Sense for real-world applications. The varying distance errors at points B and C demonstrate an error range, with a maximum error of approximately 1.130m at point B (about 0.11% of the target location) and a significant variation at point C, reaching up to 1.660m (approximately 0.15%).

Medium-term economic impacts of cover crop adoption in Maryland

Cover cropping has the potential to generate private economic benefits to farm operations as well as larger-scale environmental benefits to the broader community. However, cover crop adoption remains limited in the United States (US) (i.e., 4.7 % in 2022 (USDA-NASS, 2024)), primarily due to uncertainty in economic outcomes, with several studies showing potentially negative net returns from cover crop use in the short-term (1–3 years). This study investigates the medium-term (5–7 years) economic impact of cover crop adoption using plot-level data from field experiments in the state of Maryland. The empirical analysis employs ordinary least squares (OLS) statistical models and partial budgeting techniques to achieve the study objective. Our results show that cover crops do not have a statistically significant effect on crop yield, fertilizer costs, or pesticide costs, but we find that cover crop use statistically increases field operation and seed expenses. As a result, the private net return from cover crop adoption in the medium term is generally negative based on the Maryland field trial data used in the analysis. Specifically, the average net return per acre with cover crops is approximately $60-$90 lower for corn and around $60 lower for soybeans compared to fields without cover crops. This empirical finding suggests that public support through incentive payments may help further incentivize cover crop adoption in the US, which can then provide environmental and ecosystem services that are of benefit to the general public.

Effect of slow-release mineral fertilizer on the intensity of phosphorus and potassium mobilization in soil

The search for new technologies for effective regulation of yield and soil fertility is an urgent task. The use of slow-release fertilizers can simultaneously solve both problems – reducing fertilizer rates and increasing the mobilization of soil nutrients. In this paper, the mechanism of slow-release fertilizer effect on the mobilization rate of soil nutrients was investigated using mathematical modeling on the data of field trials with winter wheat. It is shown that in the plots with the slowest dissolving fertilizer (azofoska with 20% content of polyvinyl alcohol), the highest efficiency of azofoska was achieved (an increase in yield of 1.3 t/ha and an increase in mobile forms of phosphorus in the soil of 2.5 mg/100 g soil, potassium – 8.8 mg/100 g soil and nitrate nitrogen – 4.7 mg/100 g soil). The results of phosphorus modeling quantitatively confirmed the assumption that the low rate of dissolution of fertilizer enhances the mobilization of nutrients from soil due to the absence of an excess of plant available nutrients. We provide estimates of the polymer added efficiency for a series of polymer-modified fertilizer. It is shown that this form of fertilizer can be applied to effectively reduce the amount of fertilizer required for the planned harvest, simultaneously with the possibility of bringing the soil to a higher level of effective fertility in major nutrient elements.

Enhanced Rock Weathering for Carbon Removal-Monitoring and Mitigating Potential Environmental Impacts on Agricultural Land.

Terrestrial enhanced rock weathering (ERW) is the application of pulverized silicate rock to soils for the purposes of carbon removal and improved soil health. Although a geochemical modeling framework for ERW in soils is emerging, there is a scarcity of experimental and field trial data exploring potential environmental impacts, risks, and monitoring strategies associated with this practice. This paper identifies potential negative consequences and positive cobenefits of ERW scale-up and suggests mitigation and monitoring strategies. To do so, we examined literature on not only ERW but also industry, agriculture, ecosystem science, water chemistry, and human health. From this work, we develop recommendations for future research, infrastructure, and policy needs. We also recommend target metrics, risk mitigation strategies, and best practices for monitoring that will permit early detection and prevention of negative environmental impacts.

A Dynamic Process-Based Method for Screening Salt-Tolerant and High-Yielding Crop Varieties

Maize, the most important cereal crop worldwide, is moderately sensitive to salt stress. Given the challenges of soil salinization, developing methods to screen and cultivate salt-tolerant maize varieties is vital for enhancing food security. Among the options, process model-based assisted screening is an effective method for faster and more thorough screening of salt-tolerant varieties. In this study, a method for quickly screening salt-tolerant and high-yielding crop varieties is proposed by combining a coupled model of crop growth and water–salt response with fitness function. Then, this method was applied to the saline areas of the Hetao Plain in China to demonstrate its applicability. This study includes three parts. Firstly, field trial data from 10 commonly grown maize varieties in Hetao Plain (i.e., Xianyu 335, Yinyu 238, Jindan 73, Deke 622, DK-815, Heyu 157, Xianyu 1321, Jinrun 919, Tianci 19, and Xianyu 1225) were used to calibrate the model for different maize varieties. Moreover, model accuracy was evaluated using four indexes including the regression coefficient (b), coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). Secondly, scenario simulations were conducted using the calibrated model by setting nine initial salinity scenarios to simulate daily dynamic crop growth and soil water–salt changes for the 10 maize varieties. Finally, salt-tolerant and high-yielding maize varieties were screened based on the fitness function method during crop growth periods. The results showed that the simulation model was applicable and functioned effectively for all 10 maize varieties in the region, with the determination coefficient (R2) and Nash–Sutcliffe efficiency coefficient (NSE) of simulated plant height and leaf area index being above 0.90. Furthermore, the R2 of soil water content, soil electrical conductivity, and groundwater depth are 0.51, 0.52, and 0.60, respectively. Afterward, the fitness function values were calculated to bridge the linkage between simulated indicators and scenarios to screen varieties step by step according to the predetermined percentage of screening. Jindan 73, Xianyu 1225, and DK-815 were eventually determined as the most suitable salt-tolerant and high-yielding maize varieties. Therefore, the above results show that the proposed method is suitable for saline crop variety screening with flexibility and applicability.

Mid-season nitrogen management for winter wheat under price and weather uncertainty

ContextIn-season nitrogen (N) management tools are essential for optimizing N application rates, maximizing farmers’ economic returns and minimizing adverse environmental impacts. The primary limitation to developing such tools is the risk associated with uncertainties in weather forecasts and crop price projections required to estimate yields and returns for different N rates. Therefore, characterizing the risk associated with these uncertainties is crucial for determining optimum N rates in-season. ObjectiveThis study investigated the N application decision-making process for farmers, accounting for risks associated with weather and crop price uncertainties through crop modeling and economic analysis. MethodsWe used field trial data for winter wheat in Kansas to examine how optimal nitrogen rates and economic returns vary over sites, years, and differing farmers’ risk attitudes. First, the Environmental Policy Integrated Climate (EPIC) agroecosystem model was used to simulate the distribution of final yields under different N applications during early spring. Then, an autoregressive moving average (ARMA) model estimated the wheat price distribution at harvest based on historical prices. Finally, optimal N application rates for farmers with different risk appetites were estimated using two risk decision models: the constant-absolute-risk-averse (CARA) expected utility model, which treats upside (higher-than-expected returns) and downside (lower-than-expected returns) deviations equally, and the invariant-preference, generalized-deviation (IPGD) model, which focuses on downside risk. ResultsWe found that optimal N rates vary greatly between sites and years, as well as across farmers with different risk preferences. Due to the positive skewness of economic return distribution, farmers tend to apply lower N rates when considering downside risk. On average, the optimal N rate for farmers with a CARA coefficient of 0.002 is 77 kg/ha in the CARA model and 67 kg/ha in the IPGD model. Compared to the outcome of risk-neutral N usage, risk-averse N usage for a farmer with a CARA coefficient of 0.008 could reduce the uncertainty (standard deviation) of return by 6.2 %, on average, while the expected return decreased by only 1.2 %. ConclusionsBy lowering the N rate, risk-averse farmers would reduce the uncertainty of returns and incur a minor return loss, suggesting the possibility of improving agricultural resilience while also improving N use efficiency. Our analysis also underscores the importance of yearly site-specific N management, given the substantial variation in optimal rates across years and locations. SignificanceThis study provides the foundation for an N application decision framework that considers both weather and price uncertainty. The analysis also demonstrates the potential co-benefit of enhancing agriculture’s climate and market resilience while potentially lowering N losses.

Novel QTL Hotspots for Barley Flowering Time, Plant Architecture, and Grain Yield

Barley (Hordeum vulgare L.) is one of the oldest cultivated grains and remains a significant crop globally. Barley breeders focus on developing high-yield cultivars resistant to biotic and abiotic stresses. Barley’s flowering time, regulated genetically and by environmental stimuli, significantly impacts all of its agronomic traits, including the grain yield and plant architecture. This study aimed to detect the quantitative trait loci (QTLs) affecting these traits in 273 two-row spring barley accessions from the USA, Kazakhstan, Europe, and the Middle East across two regions of Kazakhstan, evaluating their impact on grain yield. Genotypic data were obtained from 26,529 segregating single-nucleotide polymorphisms (SNPs), and field trial data for 273 accessions, which were obtained for six traits (heading time, maturity time, vegetation period, plant height, peduncle length, and grain yield) in two regions of Kazakhstan over three growth years. As a result of a genome-wide association study (GWAS), 95 QTLs were identified for 6 agronomic traits, including 58 QTLs linked with candidate genes and/or QTLs. The remaining 37 QTLs were putatively novel, with 13 of them forming 3 QTL hotspots on chromosomes 1H (5 QTLs in the interval of 13.4–41.4 Mbp), 3H (4 QTLs in 608.6–624.9 Mbp), and 6H (4 QTLs in 553.8–572.8 Mbp). These hotspots were pleiotropic, and targeting these regions would allow breeders to enhance multiple yield-associated traits.

Including marker x environment interactions improves genomic prediction in red clover (Trifolium pratense L.).

Genomic prediction has mostly been used in single environment contexts, largely ignoring genotype x environment interaction, which greatly affects the performance of plants. However, in the last decade, prediction models including marker x environment (MxE) interaction have been developed. We evaluated the potential of genomic prediction in red clover (Trifolium pratense L.) using field trial data from five European locations, obtained in the Horizon 2020 EUCLEG project. Three models were compared: (1) single environment (SingleEnv), (2) across environment (AcrossEnv), (3) marker x environment interaction (MxE). Annual dry matter yield (DMY) gave the highest predictive ability (PA). Joint analyses of DMY from years 1 and 2 from each location varied from 0.87 in Britain and Switzerland in year 1, to 0.40 in Serbia in year 2. Overall, crude protein (CP) was predicted poorly. PAs for date of flowering (DOF), however ranged from 0.87 to 0.67 for Britain and Switzerland, respectively. Across the three traits, the MxE model performed best and the AcrossEnv worst, demonstrating that including marker x environment effects can improve genomic prediction in red clover. Leaving out accessions from specific regions or from specific breeders' material in the cross validation tended to reduce PA, but the magnitude of reduction depended on trait, region and breeders' material, indicating that population structure contributed to the high PAs observed for DMY and DOF. Testing the genomic estimated breeding values on new phenotypic data from Sweden showed that DMY training data from Britain gave high PAs in both years (0.43-0.76), while DMY training data from Switzerland gave high PAs only for year 1 (0.70-0.87). The genomic predictions we report here underline the potential benefits of incorporating MxE interaction in multi-environment trials and could have perspectives for identifying markers with effects that are stable across environments, and markers with environment-specific effects.

Using a novel multiple-source indicator to investigate the effect of scale format on careless and insufficient effort responding in a large-scale survey experiment

Common indicator-based approaches to identifying careless and insufficient effort responding (C/IER) in survey data scan response vectors or timing data for aberrances, such as patterns signaling straight lining, multivariate outliers, or signals that respondents rushed through the administered items. Each of these approaches is susceptible to unique types of misidentifications. We developed a C/IER indicator that requires agreement on C/IER identification from multiple behavioral sources, thereby alleviating the effect of each source’s standalone C/IER misidentifications and increasing the robustness of C/IER identification. To this end, we combined a response-pattern-based multiple-hurdle approach with a recently developed screen-time-based mixture decomposition approach. In an application of the proposed multiple-source indicator to PISA 2022 field trial data we (a) showcase how the indicator hedges against (presumed) C/IER overidentification of its constituting components, (b) replicate associations with commonly reported external correlates of C/IER, namely agreement with self-reported effort and C/IER position effects, and (c) employ the indicator to study the effects of changes of scale characteristics on C/IER occurrence. To this end, we leverage a large-scale survey experiment implemented in the PISA 2022 field trial and investigate the effects of using frequency instead of agreement scales as well as approximate instead of abstract frequency scale labels. We conclude that neither scale format manipulation has the potential to curb C/IER occurrence.

Comparative analysis of C n2 estimation methods for sonic anemometer data

Wind speed and sonic temperature measured with ultrasonic anemometers are often utilized to estimate the refractive index structure parameter C n 2, a vital parameter for optical propagation. In this work, we compare four methods to estimate C n 2 from C T 2, using the same temporal sonic temperature data streams for two separated sonic anemometers on a homogenous path. Values of C n 2 obtained with these four methods using field trial data are compared to those from a commercial scintillometer and from the differential image motion method using a grid of light sources positioned at the end of a common path. In addition to the comparison between the methods, we also consider appropriate error bars for C n 2 based on sonic temperature considering only the errors from having a finite number of turbulent samples. The Bayesian and power spectral methods were found to give adequate estimates for strong turbulence levels but consistently overestimated the C n 2 for weak turbulence. The nearest neighbors and structure function methods performed well under all turbulence strengths tested.

Field evaluation of spray quality and environmental risks of unmanned aerial spray systems in mango orchards

Unmanned Aerial Spraying Systems (UASS) have garnered substantial market attention as efficient tools for applying Plant Protection Products (PPPs). However, their application has primarily focused on low-growing crops such as rice and wheat. This study emphasizes the need for additional UASS field trial data, specifically addressing spray quality and environmental risks associated with 3D fruit trees. The research aims to quantitatively assess the impact of canopy structure characteristics of mango trees and droplet size on spray quality. Simultaneously, it explores the exposure risks of UASS spraying in orchards to applicators, bystanders, and machine residue, comparing these risks with traditional ground machinery. Research findings reveal substantial differences in the distribution of UASS among mango tree canopies, with variations linked to the size of the canopy. Contrary to expectations, as tree volume increases, droplet deposition does not exhibit exponential growth. More droplets are deposited in the upper part of the canopy rather than the bottom. Altering droplet size significantly influences the deposition and distribution of droplets within the mango tree canopy. Notably, the coverage effect of fine droplets at the bottom of the canopy is 2.5 times that of coarse droplets. The ground loss from ground spray guns was lower than that of UASS, but the exposure risk was higher. Specific body parts with elevated operator exposure risks are identified as the legs and chest. UASS operators are advised to remain vigilant regarding the risk of pesticide adhesion on the equipment rack.

Environmental footprint analysis of domestic air source heat pumps

Air source heat pumps (ASHP) are increasingly being recognised as a low carbon alternative to traditional fossil fuel type heating systems for residential buildings, with many countries targeting their mass deployment to meet their emissions reduction goals. However, the environmental impacts of ASHPs throughout their entire life cycle to include manufacture and end-of-life, as well as operation, have not received comprehensive attention to date. This study addresses this gap by conducting a review of research quantifying the life cycle impact of domestic ASHPs, coupled with an examination of technology uptake and deployment. By analysing the entire life cycle, from production to end-of-life, the global warming potential of a 6 kW ASHP is estimated to be 35.8 t CO2 equivalent over the lifetime of 17 years identified by the study. Additionally, the study conducts a comparative analysis of the operational footprint based on a range of electricity carbon footprint and suggests possible improvement of 1200 % with targeted improvments. Furthermore, the study calculates the global warming potential using both the seasonal performance factor specified by a heat pump manufacturer and real-world field trial data. The findings unveil a significant disparity of 20 % between the two methodologies, underscoring the paramount importance of incorporating in-field heat pump performance data when evaluating their environmental impact.

TBMF Framework: A Transformer-Based Multilevel Filtering Framework for PD Detection

Partial discharge (PD) of overhead lines is an indication of imminent dielectric breakdown and a cause of insulation degradation. Efficient PD detection is the significant foundation of electrical system maintenance. This paper proposes a transformer-based multilevel filtering (TBMF) framework for PD detection. It creates the multilevel filtering mechanism to be robust to large-scale industrial measurements contaminated with a variety of background noises and plenty of invalid information. The primary filtering innovatively creates the principle of possible PD measurements to replace feature extraction and reduce manual intervention. For the first time, multiple transformer-based algorithms are introduced to the PD detection field to process the possible PD measurements without relying on the sequence order. The secondary filtering then refines the segmentation-level results from the primary filtering and outputs the overall detection results. Multiple numerical algorithms, AI models, and intelligent meta-heuristic optimization have been adopted as methodologies of the secondary filtering. The TBMF framework is experimentally verified by extensive field trial data of medium voltage overhead power lines. Its detection accuracy reaches 96.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , which outperforms other techniques in the literature. It provides an economic and complete PD detection solution to maintain the economical and safe operation of power systems.

Leveraging digital agriculture for on-farm testing of technologies

The Precision Nitrogen Project (PNP) worked with more than 80 corn and winter wheat producers to inexpensively design and implement randomized, replicated field strip trials on whole commercial farm fields, and to provide site-specific testing of current nitrogen (N) technologies. This article proposes a conceptual framework and detailed procedure to select the N technology to be tested; design and implement field trials; generate, process, and manage field trial data; and automatically analyze, report, and share benefits from precision N technology. The selection of the N technology was farmer-driven to ensure a good fit and to increase the likelihood of future technology adoption. The technology selection method was called the “N tiered approach”, which consisted of selecting a technology that progressively increases the level of complexity without exceeding the farmer’s learning process or farm logistic constraints. The N tools were classified into (1) crop model-based, (2) remote sensing-based, (3) enhanced efficiency fertilizers, and (4) biologicals. Field strip trials comparing producers’ traditional management and the selected N technology were combined with site-specific N rate blocks placed in contrasting areas of the fields. Yield data from the N rate blocks was utilized to derive the site-specific optimal N rate. The benefits of current N technologies were quantified by comparing their yield, profit, and N use efficiency (NUE) to growers’ traditional management and to the estimated site-specific optimal N rate. Communication of the trial results back to the growers was crucial to ensure the promotion and adoption of these N technologies farm wide. The framework and overall benefits from N technologies was presented and discussed. The proposed framework allowed researchers, agronomists, and farmers to carry out on-farm precision N experimentation using novel technologies to quantify benefits of digital ag technology and promote adoption.

The Wetting Characteristics and Microscopic Wetting Mechanism of Coal under High-Pressure Nitrogen Environment

The wettability of coal is an important factor influencing hydraulic stimulation. Field-trial data has proven that high-pressure N2 injection plays a positive role in increasing the coalbed methane (CBM) production rate. For the purpose of investigating the mechanism by which N2 promotes the gas rate, multiple experiments were conducted sequentially on the wettability of anthracite under different N2 pressures. Testing of the coal surface contact angle was conducted under 0.1–8 MPa nitrogen pressure using a newly built contact angle measuring device. The coal samples were collected from the Xinjing Coal Mine in the Qinshui Basin, China. The test results revealed that the contact angle increased with increasing N2 pressure. That is, the contact angle was 77.9° at an N2 pressure of 0.1 MPa and gradually increased to 101.4° at an infinite N2 pressure. In contrast, the capillary pressure decreased with an increasing N2 pressure, from 0.298 MPa to −0.281 MPa. The relationship between contact angle and N2 pressure indicated that the wettability was reversed at a N2 pressure of 5.26 MPa, with a contact angle of 90° and a capillary pressure of 0 MPa. The capillary pressure reversed to a negative value as the N2 pressure increased. At the microlevel, a high N2 pressure increases the surface roughness of coal, which improves the ability of the coal matrix to adsorb N2, forming the gas barrier that hinders the intrusion of water into the pores of the coal matrix. The results of this study provide laboratory evidence that high-pressure N2 injection can prevent water contamination and reduce the capillary pressure, thus benefiting coalbed methane production.

Practical Considerations in Item Calibration With Small Samples Under Multistage Test Design: A Case Study

The multistage testing (MST) design has been gaining attention and popularity in educational assessments. For testing programs that have small test‐taker samples, it is challenging to calibrate new items to replenish the item pool. In the current research, we used the item pools from an operational MST program to illustrate how research studies can be built upon literature and program‐specific data to help to fill the gaps between research and practice and to make sound psychometric decisions to address the small‐sample issues. The studies included choice of item calibration methods, data collection designs to increase sample sizes, and item response theory models in producing the score conversion tables. Our results showed that, with small samples, the fixed parameter calibration (FIPC) method performed consistently the best for calibrating new items, compared to the traditional separate‐calibration with scaling method and a new approach of a calibration method based on the minimum discriminant information adjustment. In addition, the concurrent FIPC calibration with data from multiple administrations also improved parameter estimation of new items. However, because of the program‐specific settings, a simpler model may not improve current practice when the sample size was small and when the initial item pools were well‐calibrated using a two‐parameter logistic model with a large field trial data.

Adapting the grassland model BASGRA to simulate yield and nutritive value of whole-crop barley

Ensiling of whole-crop biomass of barley before full maturity is common practice in regions with a short growing season. The developmental stage of barley at harvest can have a large impact on yield and nutritive composition. The relationships between crop growth, environmental conditions and crop management can be described in process-based simulation models. Some models, including the Basic Grassland (BASGRA) model, have been developed to simulate the yield and nutritive value of forage grasses, and usually evaluated against metrics of relevance for whole-crop silage. The objectives of this study were to: i) modify the BASGRA model to simulate whole-crop spring barley; ii) evaluate the performance of this model against empirical data on dry matter (DM) yield and nutritive value attributes from field experiments, divided into geographical regions; and iii) evaluate DM yield, nutritive value and cutting date under current and future climate conditions for three locations in Sweden and four cutting regimes. Main model modifications included addition of a spike pool, equations for carbon (C) and nitrogen (N) allocation to the spike pool and equations for C and N translocation from vegetative plant parts to spikes. Model calibration and validation against field trial data from Sweden, including samples harvested from late anthesis stage to hard dough stage that were either pooled or divided into regions, showed better prediction accuracy, evaluated as normalised root mean squared error (RMSE), of neutral detergent fibre (NDF) (7.58–18.4%) than of DM yield (16.8–27.8%), crude protein (15.5–23.2%) or digestible organic matter in the DM (DOMD) (12.0–22.2%). Model prediction using weather data representing 1990–2020 and 2021–2040 climate conditions for three locations in Sweden (Skara, Umeå, Uppsala) showed lower DM yield, earlier harvest and slightly higher NDF concentration on average (across locations and developmental stage at cutting) when using near-future climate data rather than historical data. The model can be used to evaluate whole-crop barley performance under production conditions in Sweden or in other countries with similar climate, soils and crop management regimes.

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp.

Genome-wide association analysis identifies a consistent QTL for powdery mildew resistance on chromosome 3A in Nordic and Baltic spring wheat

Key messageQPm.NOBAL-3A is an important QTL providing robust adult plant powdery mildew resistance in Nordic and Baltic spring wheat, aiding sustainable crop protection and breeding.Powdery mildew, caused by the biotrophic fungal pathogen Blumeria graminis f. sp. tritici, poses a significant threat to bread wheat (Triticum aestivum L.), one of the world’s most crucial cereal crops. Enhancing cultivar resistance against this devastating disease requires a comprehensive understanding of the genetic basis of powdery mildew resistance. In this study, we performed a genome-wide association study (GWAS) using extensive field trial data from multiple environments across Estonia, Latvia, Lithuania, and Norway. The study involved a diverse panel of recent wheat cultivars and breeding lines sourced from the Baltic region and Norway. We identified a major quantitative trait locus (QTL) on chromosome 3A, designated as QPm.NOBAL-3A, which consistently conferred high resistance to powdery mildew across various environments and countries. Furthermore, the consistency of the QTL haplotype effect was validated using an independent Norwegian spring wheat panel. Subsequent greenhouse seedling inoculations with 15 representative powdery mildew isolates on a subset of the GWAS panel indicated that this QTL provides adult plant resistance and is likely of race non-specific nature. Moreover, we developed and validated KASP markers for QPm.NOBAL-3A tailored for use in breeding. These findings provide a critical foundation for marker-assisted selection in breeding programs aimed at pyramiding resistance QTL/genes to achieve durable and broad-spectrum resistance against powdery mildew.