约翰迪尔

Efficient nitrogen (N) management in maize (Zea mays L.) is critical to achieving high yields. However, N overapplication remains widespread, driven by concerns over potential yield losses. Understanding tolerance of maize to early-season N deficiencies is essential for developing effective in-season strategies. This study evaluated different in-season N fertilization strategies and their effects on crop growth rate (CGR), leaf area index (LAI), and plant N status (via chlorophyll meter) throughout the season. The study also assessed the impact on yield and yield components to determine recovery ability from temporary N deficiencies. Two field trials were conducted in Kansas, United States, during 2023 and 2024 under irrigated conditions. Treatments comprised N rates ranging from 0 to 180 kg ha-1 applied at planting or split between planting (90 kg N ha-1) and V6, V10, or V14 (30-90 kg N ha-1). Nitrogen deficiencies during vegetative growth reduced CGR and kernel number, but plant N status recovered even with N applied as late as V14. Kernel weight was positively associated with CGR during grain filling, highlighting the importance of crop N status to maximize kernel development and yield. The NSI was more responsive than LAI to N fertilization timing. Results showed that maize can tolerate early-season N deficiencies without yield penalties if plant N status is corrected timely, under the tested yield levels. These findings underscore the importance of implementing timely in-season N strategies to mitigate N stress and promote balanced productivity and sustainability.

Digital traceability of engineering product characteristics is foundational to Model-Based Enterprise (MBE) implementation [1, 2, 3]. While characteristics in the product definition (drawings or Model-Based Definition (MBD)) are increasingly well structured, characteristics originating from supplemental specifications (internal company documents and industry standards) remain difficult to formalize and trace. This gap drives inconsistent application of specifications, labor-intensive interpretation of text-heavy documents, and limited traceability from specification requirements to the features they affect.This paper presents a system architecture and prototype implementation for encoding specifications as machine-executable rules that deter-ministically apply product characteristics to features based on the product definition. Individual specification clauses are modeled as conditional statements (antecedent-consequent pairs) and implemented as an extension to QIF Rules, part of the ISO Quality Information Framework (QIF) standard. Rules are applied to identified features and other entities in the product definition, creating or tightening characteristic requirements according to specification requirements.The paper describes the implementation of a prototype QIF Rules extension and software tool, documenting difficulties with digitizing paper-based specifications (ambiguity, contradictions, inconsistent interpretation) and the process of formalizing them into executable rules. The prototype demonstrates how machine-executable specifications increase consistency in rule application, enable automation, and provide auditable, bidirectional traceability between specification clauses and product characteristics. Implementation challenges (ambiguity resolution, tool development, user adoption) are discussed, and future work is outlined, including broader standards coverage, authoring tool development, and potential incorporation into the QIF standard. The work aligns with community initiatives to make standards machine-interpretable, such as SAE ITC’s Digital Standards Alliance (DSA) and the ISO/IEC SMART programme.

Due to the tall nature of self-propelled sprayers and their high center of gravity, operators can experience intense amounts of movement when these machines come to an abrupt stop or turn sharply at high speeds. For this reason, it is important to understand the chassis suspension and how different design factors affect machine dynamics. This research focused on the chassis suspension strut assembly of a 408R John Deere Self-Propelled Sprayer; this assembly contains grease locations that operators are recommended to grease at regular intervals of operation. To determine the machine performance differences due to grease variability, testing was conducted on a test stand and in the field with two primary suspension configurations: (1) a "dry" configuration with the suspension spindles wiped clean of all grease from the joint, and (2) a "fully greased" configuration. A comprehensive data acquisition package was strategically installed on the machine before conducting testing to derive an A to B comparison of chassis suspension dynamics, operator comfort, and motion stability of both the chassis and boom. The results proved that the state of grease on the suspension spindles dramatically affected the machine's dynamic performance. The results revealed that when the machine was appropriately greased, the operator was more comfortable, but at the cost ofproducing a less stable chassis and boom system. In contrast, insufficient grease increased chassis and boom stability, but the rigidity resulted in the operator experiencing more discomfort due to absorbing high amounts of accelerations. Overall, the testing provided insightful machine performance differences to better understand the caused a customer's maintenance level.

This work aims to develop a cost-effective Machine Learning (ML) based methodology to identify different implements like primary tillage implements, secondary tillage implements, other implements like Loader, snow blower, etc. For this study, engine, vehicle, and beacon data were collected using a data logger mounted on vehicles operating with two primary implements: a loader and a secondary tillage implement. Firstly, time series data has been examined for noise, peaks, and missing values. Following the parameter analysis, parameters displaying trends or specific patterns for different implements have been selected. By selecting the most effective parameters, various classification machine learning algorithms, such as KNN, Decision Tree, and Support Vector Machine (SVM), have been developed and trained to accurately identify the implement attached to the vehicle. The developed methodology was tested on new data and demonstrated an average accuracy of 92.5