Effective control of exhaust emissions from modern diesel engines requires the use of aftertreatment systems. Elevated aftertreatment component temperatures are required for engine-out emissions reductions to acceptable tailpipe limits. Maintaining elevated aftertreatment components temperatures is particularly problematic during prolonged low speed, low load operation of the engine (i.e. idle, creep, stop and go traffic), on account of low engine-outlet temperatures during these operating conditions. Conventional techniques to achieve elevated aftertreatment component temperatures include delayed fuel injections and over-squeezing the turbocharger, both of which result in a significant fuel consumption penalty. Cylinder deactivation (CDA) has been studied as a candidate strategy to maintain favorable aftertreatment temperatures, in a fuel efficient manner, via reduced airflow through the engine. This work focuses on prediction and demonstration of fuel economy benefits of CDA when implemented at idle and low load portions of the emission certification cycles, such as the heavy duty federal test procedure (HD-FTP), and other real-world drive cycles, including the Orange County bus and port drayage creep cycles. A 3.4% benefit in fuel economy has been demonstrated over the HD-FTP, while maintaining tailpipe-out NOx emissions. Greater improvements in fuel economy have been predicted over the real world cycles, with a 5.6% reduction predicted over the Orange County bus cycle and 35% reduction predicted over the port drayage creep cycle.

Crop residue plays a crucial role in diverse areas of soil, biomass, crop yield, and the environment by impacting aspects such as soil organic matter, erosion, nutrient leaching, soil moisture and temperature, plant emergence, in turn influencing plant biomass and crop yields. Measuring crop residue coverage has been a long-term practice, primarily for monitoring purposes by agencies for various programs. More recently, increased research effort has been focused on improving the means and mechanisms of measuring and sensing crop residue efficiently and on large scales for both monitoring and improved agricultural decision-making insights. This review attempts to provide an overview of crop residue measurement and sensing, presenting a holistic review of the different measurement techniques available. It also highlights how research has shifted from field-based measurement to the use of diverse sensing approaches and future directions. Traditional methods of crop residue measurement are reviewed and the literature on more modern sensing approaches is summarized through categorization of the approaches based on sensing scale of satellite-, UAV-, and ground-based systems. Key findings highlight the growing shift toward high-resolution UAV sensing and the increasing use of machine learning models for improved classification. Identified critical challenges, particularly in satellite sensing, include spectral similarity between residue and soil, influence of moisture, and limited real-time application. The review aims to provide a summary of the current state of crop residue sensing with insights into potential areas for opportunities, especially through on-machine sensing systems, emphasizing the need for real-time, integrated, and adaptive residue coverage sensing solutions.

Crop residue plays a significant role on soil health, nutrient cycling, and crop yield, making residue management a key consideration in agricultural production. This review aims to synthesize current knowledge on two different aspects of crop residue in grain production systems: common management practices and their effects. A comprehensive literature survey was conducted using the Scopus database, covering worldwide publications from 1990 to 2025. First, this review explores various crop residue management practices, including burning, stubble retention, and different tillage methods (conservation tillage vs. conventional tillage). This section mainly focuses on tillage practices with an emphasis on the environmental, economic, and agronomic consequences of each approach. Then this review paper explores the effects of crop residue on soils and ecosystems, including the role of crop residues in improving soil quality and enhancing soil organic carbon sequestration. The interaction between crop residues and hydrological processes such as water infiltration and runoff is also examined in this section. The relationships between residue management practices and crop growth and yield responses are also critically assessed. This review highlights the shift from conventional, environmentally detrimental practices toward conservation-oriented approaches, especially reduced tillage, no tillage, and residue retention. Key knowledge gaps and research priorities are identified, particularly regarding crop residue effects on deep soil carbon sequestration, hydrology, crop growth and yields, and the lack of practical management guidelines across agroecosystems. This review provides an integrated framework to support crop residue management and guide future research in conservation agriculture.

Crop residue management is critical for soil conservation and agricultural productivity. Growers actively manage the crop residue in their fields based on experiential observation; however, residue management has thus far been largely omitted from typical modern precision agriculture workflows due to the lack of reliable sensing. The widely used baseline method for measuring crop residue coverage (CRC), the manual line-transect method, requires manual fieldwork that limits its utility. Other methods for CRC measurement and estimation rely on orthoimagery, which neglects the potential use of on-machine imagery collected during agricultural operations. This study first analyzes the accuracy of the manual line-transect method and develops alternatives for measuring CRC using images captured by onboard cameras during tillage operations. The proposed virtual scatter method is shown to achieve a standard deviation of 3.7% compared to the manual line-transect method. Then, the virtual scatter method is subsequently employed to generate CRC labels for an image dataset, in which residue pixels were annotated. Residue masks are then generated at two resolutions (pixel-level and superpixel-level) and used as inputs to segmentation models. A comparison of various machine learning techniques to estimate CRC using on-machine imagery, including Support Vector Machine (SVM), Random Forest (RF), and Entangled Random Forest (ERF) at two different resolutions, is presented. The results demonstrate the superior performance of the ERF model at the superpixel level compared to traditional SVM and RF models, in addition to reduced compute times.