Articles and Scholarly Works

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Protección de los polinizadores en entornos agrícolas
(Pollinator Partnership, 2025-10-27) Grinstead, Andy; Lievers, Reed; Morandin, Lora; Redfield, Phoebe; Wilson, Cody; DiGiacomo, Gigi; Hutchinson, Bill; Illes, Molly; Lee, Katie; Schuh, Marissa; Wimmer, Madeline; Ziegler, Ben
You are an Expert: Research collaborations for survivors
(2025) Ayler, Tonique; Forliti, Teresa; Friedman, Joy; Mariotti, Mikki; Nelson, Christine
2024-2025 Greater Voyageurs Ecosystem Wolf Population Report
(2025) Gable, Thomas; Homkes, Austin; Heny, Sophie; Bump, Joseph
During April 2024–April 2025, we studied the wolf population in the Greater Voyageurs Ecosystem (GVE), Minnesota to understand wolf population dynamics and how changes in population dynamics are connected to or influence predation behavior, wolf pup survival, and changes in prey density. To estimate wolf population size and assess wolf population dynamics, we used locations from GPS-collared wolves to estimate the size and distribution of wolf territories in the GVE, and deployed 378 trail cameras across the GVE from December 1, 2024 to April 10, 2025. We estimate wolf population density in the Greater Voyageurs Ecosystem was 44.7 wolves/1000 km2 in 2024–2025, a 19% decrease in wolf population density from last year (2023–2024 density: 55.1 wolves/1000 km2) and a 31% decrease from two years ago (2022–2023 density: 64.8 wolves/1000 km2). Average pack size in 2024–2025 was 4.05 wolves/pack, a slight decrease from 2022–2023 and 2023–2024 when average pack size was 4.2 wolves/pack and 4.3 wolves/pack respectively. Thus, the decrease in wolf population density can be attributed predominantly to a substantial increase in territory size in combination with a considerable decrease in recruitment. Despite the recent decrease in the population, all evidence indicates that the wolf population in the Greater Voyageurs Ecosystem is a fairly stable, high-density wolf population.
Processing Pea Variety Trial, 2025
(2025-10-27) Rohwer, Charlie
Pea yields in our trial were outstanding in 2025, and anecdotally, regional yields were very good as well. Soil conditions were rather wet for the early planting in some portions of the field, but the heaviest rain was restricted to late June and early July, during harvest. This was not problematic for our hand-harvested plots. Moderate temperatures certainly helped as well. High temperatures >87ºF were only recorded 4 days in all of June and 3 days in early July, as peas flowered and matured. Four days throughout the season saw stressful low daily temperature above 69ºF.
Nepali Speaking Bhutanese Refugees Agricultural Wisdom Reviving Traditional Practices for a Sustainable Future
(2025) Dhakal, Narayan
The Bhutanese refugee community in Minnesota exemplifies the resilience of traditional agricultural practices amidst modern challenges. Many of these refugees, originating from subsistence farming backgrounds, have revitalized their cultural heritage through community gardens, growing a diverse range of organic vegetables and cereals. Their farming practices, rooted in Himalayan traditions, emphasize sustainability through techniques such as crop rotation, organic fertilization, and seed preservation. Although their agricultural wisdom aligns with global agroecological and regenerative farming movements, barriers like limited land access, language difficulties, and unfamiliarity with U.S. market dynamics restrict their growth potential. Collaborative initiatives with academic institutions, nonprofits, and local organizations have facilitated small-scale organic farming and cultural integration. However, engaging younger generations in farming and expanding market access remain pressing concerns. Recommendations include fostering cooperative models, introducing niche crops to farmers' markets, and bridging technological and linguistic divides. These efforts aim to integrate Bhutanese practices into broader climate resilience and food sovereignty frameworks, while enhancing immigrant empowerment. By blending their ancestral knowledge with modern innovations, Bhutanese farmers contribute to sustainable agriculture, biodiversity conservation, and climate justice. Their story serves as a compelling model for inclusive sustainability and the revitalization of traditional wisdom in new contexts.
CSLearning AID Checklist: A Research-Informed Tool for Inclusive K-8 CS Integration
(2025) Cannady, Justin; Rosato, Jennifer; Schonfeld, Paul; Cozzolino, Tom
High-quality, inclusive computer science (CS) integration in K–8 education remains inconsistent, with many lessons lacking clear structure, accessible design, or attention to equity. This article presents the CSLearning AID Checklist, a research-informed tool created to help educators and curriculum designers evaluate and strengthen CS-integrated lessons. Developed through an iterative co-design process grounded in Universal Design for Learning, culturally responsive-sustaining pedagogy, and established CS education frameworks, the checklist comprises 45 criteria across five domains: Composition, Student Learning, Assessment, Inclusion, and Digital Accessibility. We describe the theoretical foundations that shaped the checklist, the multi-phase refinement process involving researchers, practitioners, and content experts, and the resulting structure that supports both practical classroom use and systematic curriculum review. The article also illustrates how the checklist clarifies expectations for lesson design, highlights gaps in accessibility and equity, and provides a shared language for professional development, coaching, and research. By offering a rigorous yet classroom-ready tool, the CSLearning AID Checklist contributes to ongoing efforts to broaden participation in computing. It supports educators in designing lessons that are pedagogically sound, culturally responsive, and accessible to diverse learners, while giving researchers a structured framework for analyzing instructional materials and advancing equity-focused CS education.
Support and Guide: Observational Coding Scale Manual for Primary Care Clinician Conversations with Parents
(2025) Mehus, Chris; Ballard, Jaime; Driscoll, Janette; Sargeant, Laura; Exsted, Marci
Geometry-based mass grading of mango fruits using image processing
(2017-05-13) Rahman, Towfiq; Momin, Abdul; Sultana, M.S.; Igathinathane, C.; Ziauddin, A.T.M.; Grift, T.E.
Mango (Mangifera indica) is an important, and popular fruit in Bangladesh. However, the post-harvest processing of it is still mostly performed manually, a situation far from satisfactory, in terms of accuracy and throughput. To automate the grading of mangos (geometry and shape), we developed an image acquisition and processing system to extract projected area, perimeter, and roundness features. In this system, images were acquired using a XGA format color camera of 8-bit gray levels using fluorescent lighting. An image processing algorithm based on region based global thresholding color binarization, combined with median filter and morphological analysis was developed to classify mangos into one of three mass grades such as large, medium, and small. This system achieved an accuracy of 97% for projected area and Feret diameter, 79% for perimeter, and 36% for roundness. To achieve a finer grading, two different grading features could be used in sequence. The image grading system is simple and efficient and can be considered a suitable first stage to mechanizing the commercial grading of mangos in Bangladesh. Moreover, the method has the potential to be applied to other crops with suitable adjustments.
Characterization of tea (Camellia sinensis) granules for quality grading using computer vision system
(2021-09-22) Rahman, Towfiq; Ferdous, Sabiha; Jenin, Mariya; Mim, Tanjina; Alam, Masud; Mamun, Muhammad
Tea (Camellia sinensis) has been found as an important medicinal beverage for human which is consumed all over the world. Primarily, the majority of tea is being cultivated in Asia and Africa, however it is commercially produced by more than 60 countries. Though substantial amount is produced, its processing system is still underdeveloped which leads to decrease in export opportunity as well as low monetary value. Moreover, the traditional method of tea grading and sorting is laborious, inefficient, and costly which ultimately produces the low-quality heterogeneous products. Processing and grading of tea granules after drying is very important task for maintaining quality. Computer vision (CV) applications in processing unit especially in grading and sorting of agro-products is very popular and reliable option to improve quality of produce. In this study, an attempt was taken to develop a machine vision system for quality grading of tea granules based on physical parameters of four standard tea grades namely BOP, GBOP, CD and PF. An image acquisition system with suitable illumination arrangement was developed to obtain high resolution image of tea granules. The images were analyzed to extract physical features like projected area, circularity, roundness, ferret diameter, aspect ratio and solidity. Tea granules (BOP, CD, PF and GBOP grade) were found significantly different for the textural features area, perimeter, circularity, roundness and ferret diameter. Projected area, perimeter, and feret diameter treated as a good indicator of the extracted features as the system has been able to significantly (p < 0.01) differentiate among the grade of tea. The developed characterization attributes based on physical features prior to an automatic sorting technology will improve the efficiency and enhance the cost-effectiveness which ultimately led to energize the international export market.
Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets
(2023-12-13) Rahman, Towfiq; Brown-Brandl, Tami; Rohrer, Gary; Sharma, Sudhendu; Manthena, Vamsi; Shi, Yeyin
High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (P < 0.05). Landrace-sired litters had a PWM of 16.26% (±0.13), whereas Yorkshire-sired litters had 15.91% (±0.13). PWM increased with higher parity orders (P < 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86). However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period.
Present Mechanization of Regional Tea Processing and Grading System in Bangladesh
(International Conference on Sustainable Agriculture and Rural Development: Road to SDGs, 2020-01) Mamum, Muhammad; Rahman, Towfiq; Rahman, M.; Hamid, K.
Nowadays tea is the most popular beverage crop of Bangladesh which contributes to our national GDP. There are 166 tea gardens in the various region in Bangladesh are producing tea. Some best quality tea is exporting from our country but this amount is negligible due to improper handling and processing systems. Quality tea production from processing houses is beyond control and mostly depends on the processing method. Improper handling and insufficient technology results in lower grade tea, as well as foreign contaminates, are found in made tea. This research focused on the problems of the present of tea processing and handling scenario. Tea gardens from Sylhet, Moulovibazar, and Panchagarh region were visited to find out real-life problems and probable solutions by an interview schedule. Tea processing technology includes plucking, withering, cutting-curling-twisting, rolling, sorting, and packaging was considered to visualize the contributions from labor groups, transportations, and types of machinery. A quantitative cost analysis was also analyzed to find the scope of appropriate mechanization in tea industries. The study results that, due to improper withering, high transportation cost, uneven drying, and testing methods cause low-grade tea to compete for the international market. Moreover, it has been seen that fuel cost increased 13% and plucking cost decreased 23 % over the past 20 years. The study concludes that an appropriate scale mechanization policy and adoption of new machines must be adopted immediately considering each tea processing house. If the processing technology can be simulated by modern techniques and mechanical aids the goal for better quality tea can be achieved and foreign exchange can be assured for the sustainable tea industry in Bangladesh.
Effect of urea fertilizer deep placement days after transplanting using brri prilled urea applicator on transplanted boro rice yield
(2020) Mamun, Muhammad; Nahar, K.; Rahman, Towfiq; Hossen, Md Anwar
Rice is the most important crop in the developing countries of Asia. In the south and south-east Asia, rain-fed and irrigated transplanted rice occupies nearly two-thirds of the rice-growing area and produces more than 80% of the rough rice. In these areas, prilled urea conventionally applied by farmers is very insufficient in the transplanted rice field, where severe losses occur (up to 60% of applied N) via NH3volatilization, denitrification, leaching, and runoff. Considering loss minimization, an experiment was conducted during the Boro season at Bangladesh Agricultural Development Corporation (BADC) in Sylhet to evaluate the performance of BRRI Prilled Urea Applicator (BPUA) at the different periods after transplanting BRRI dhan28. The results reveal that the field performance of the BPUA was suitable on first day after seedling transplanting under sandy clay loam soil compared to the third day after transplanting (DAT). At the 105 DAT, the height of the crop was found to be 104.3, 104.3, and 95.7 cm for urea deep placement by BPUA on first, second, and third day after seedling transplanting respectively. The maximum grain and straw yield was found at 6.8 t ha-1 and 5.2 t ha-1, respectively which varied with the date of applicator operation after seedling transplanting. The benefit-cost ratio was found 1.63 at first DAT, whereas it was lower on the third days after seedling transplanting. Farmer can apply urea fertilizer in the non-oxidized zone by the BPUA after the first and second day of seedling transplanting in the sandy clay loam soil for maximum yield.
Classification of Sow Postures Using Convolutional Neural Network and Depth images
(American Society of Agricultural and Biological Engineers, 2024) Rahman, Md Towfiqur; Brown-Brandl, Tami; Rohrer, Gary; Sharma, Shudhendu
The US swine industry reports an average preweaning mortality of approximately 16% where approximately 6% of them are attributed to piglets overlayed by sows. Detecting postural transitions and estimating sows’ time budgets for different postures are valuable information for breeders and engineering design of farrowing facilities to eventually reduce piglet death. Computer vision tools can help monitor changes in animal posture accurately and efficiently. To create a more robust system and eliminate varying lighting issues within a day including daytime/ nighttime differences, there is an advantage to using depth cameras over digital cameras. In this study, a computer vision system was used for continuous depth image acquisition in several farrowing crates. The images were captured by top down view Kinect v2 depth sensors in the crates at 10 frames per minute for 24 h. The captured depth images were converted into Jet colormap images. A total of 14277 images from six different sows from 18 different days were randomly selected and labeled into six posture categories (standing, kneeling, sitting, sternal lying, lying on the right and lying on the left). The Convolutional Neural Network (CNN) architectures, that is, Resnet-50, Inception v3 with ‘imagenet’ pre-trained weight, were used for model training and posture images were tested. The dataset was randomly split training (75%) and validation (roughly 25%) sets. For testing, another dataset with 2885 images obtained from six different sows (from 12 different days) was labelled. Among the models tested in the test dataset, the Inception v3 model outperformed all the models, resulting in 95% accuracy in predicting sow postures. We found an F1 score between 0.90 and 1.00 for all postures except the kneeling posture (F1=0.81) since this is a transition posture. This preliminary result indicates the potential use of transfer learning models for this specific task. This result also indicates that depth images are suitable for identifying the postures of sows. The outcome of this study will lead to the identification and generation of posture data in a commercial farm scale to study the behavioral differences of sows within different characteristics of farm facilities, health status, mortality rates, and overall production parameters.
Microcontroller based granular urea application attachment for rice transplanter
(Journal of Bangladesh Agricultural University, 2019-09-30) Rahman, Towfiq; Alam, Md Monjurul; Hossain, Md Mosharraf; Mamun, Muhammad
Transplanting and fertilizer application for rice production in Bangladesh are tedious, time consuming and laborious task, and mostly done manually. Mechanical transplanting of rice becoming popular in the country in recent years and few machines have been developed for granular urea deep placement, however, having some limitations. Placing granular urea precisely along with rice transplanting, an attempt was under taken to design and fabricate an electronic control granular urea applicator to be attach with a 4-row walk behind type rice transplanter. Fabrication of the electronic granular urea applicator was done in the workshop of the Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh. Physical structure of the attachment was assembled with available parts of BARI granular urea applicator. A DC gear motor was coupled with metering disk shaft to rotate and pick granular urea from hopper. Moreover, its speed was synchronized with the picker speed of the rice transplanter by a microcontroller Arduino Mega 2560. A computer program was developed and compiled successfully into Arduino IDE, where an equation was derived and incorporated into loop control structure. The program can also be used for any kind of applications where variable rate is required. The machine was found successful in test run and laboratory-based experiments. Average spacing of granular urea placement was found 34.71 cm with 1.38% missing hill, Its power requirement was found about 20 W. This innovation provided options for performing granular urea application and rice transplanting, two most laborious tasks simultaneously which might minimize the cost of production as well as human drudgery with an error free manner.
Mango Surface Color Features Measurement Using Digital Image Processing
(Journal of Agricultural, Environmental and Consumer Sciences, 2022) Rahman, Towfiq; Kumar, Goutom; Momin, Abdul
Postharvest processing of agricultural produce is still done the conventional way in Bangladesh. Manual grading of agricultural produce, especially fruits and vegetables, is laborious and costly due to acute shortage of labor during the peak season, as well as difficulty maintaining the product quality. Machine vision system (MVS) applications are widely used nowadays as a non-destructive and cost-effective technology for automatically grading and sorting large volumes of produce in the packing house according to size, shape, color, texture, and surface defects. In this study, a simple MVS was constructed measuring different color features of mango fruit surface as a part of developing an automatic grading system. A CCD camera with a fluorescent lighting system was incorporated for acquiring images of mangos. Different color properties were extracted from the acquired images and analyzed. The best-suited color information (HSI model) was found so that the fruits can be separated from their background easily and differentiated. The measured color information will be further used for developing a grading algorithm based on different features of mango, further aimed to develop an automatic mango grading system.
Influences of artificial light on mating of black soldier fly (Hermetia illucens)—a review
(2022-04-15) Awal, Md. Rabiul; Rahman, Md Masudur; Choudhury, Md Abdur; Hasan, Md Mehedi; Rahman, Towfiq; Mondal, Md Fuad
Black soldier fly (Hermetia illucens) is a potential insect species which can convert biodegradable materials and some indigestible organic waste into valuable biomass. Because of having good quality of fat and protein, its production and use in animal feed are being extended day by day. To fulfill the future demand re-searchers are trying to find out the successful mass rearing techniques of H. illucens in laboratory or indoor condition. However, the most critical part of H. illucens mass production is obtaining successful mating. This insect is very sensitive to light. It prefers direct sunlight for its successful mating however, artificial light has substantial effects on its mating behaviors. It was reported that light quality, intensity, duration have signifi-cant influences on the H. illucens successful mating and fertilized egg production. This review brings in forth all the information about artificial light effects on H. illucens adults for their successful mating towards the mass production in indoor condition.
Leaf-Based Varietal Categorization of Sweetpotato (Ipomoea batatas L. Lam.), a Potentially Healthful Vegetable, Using Image Processing and K-Means Clustering
(Journal of Agriculture, Food, Environment and Animal Sciences, 2025-06-01) Islam, Shahidul; Rahman, Towfiq; Islam, Md Hamidul; Momin, Md Abdul
Sweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected and pre-processed to extract relevant features. A distinct difference in leaf physical characteristics, i.e., leaf area, perimeter, circularity factor, breadth, and leaf ratio, between the two varieties was observed. K-means clustering algorithm identified two sweetpotato varieties as distinct clusters with centroid values (Cluster 0: Area 695627 and Cluster 1: Area 525895). Results revealed that sweet potato leaves in cluster 0 tend to have more prominent physical characteristics than in cluster 1. This result demonstrates the prospects of using machine learning and image processing techniques for in situ varietal classification of sweetpotato. The results bridge the visual characteristics and their quantitative assessment, fostering a deeper understanding of the plant's phenotype and supporting advancements in agriculture, research, and crop improvement.
Effect of rotary blade modification on residue retention into conservation agriculture practices
(AgricEngInt: CIGR Journal, 2020-02-09) Mahmud, Md Talash; Rahman, Towfiq; Hossain, Md Mosharraf; Rabbani, Muhammad
Conservation Agriculture (CA) has been a promising technique for better crop production. Manual seeding with conventional tillage practice is laborious and time consuming. However, Strip tillage (ST) method incorporating seeding machines minimizes human drudgery and optimizes the crop yield. Many problems associated with ST have been rectified e.g. tiny furrow backfill, inaccuracy of seed and fertilizer placement, leading to poor germination and curtailed outcomes. This article focused on the effects of residue retention for the rotary blades design on a versatile multi-crop planter (VMP). Four types of rotary blades of VMP differed by 15o increment of tip angles were designed, fabricated, and experimented with a constant speed 350 RPM for ST operation targeting wheat and maize cultivation. Technical aspects related to the quality of strip i.e. width of furrow, depth of seed placement, moisture content, bulk density etc. were observed. Furthermore, the percentage of straw cut and seed emergence were visualized. From the observation, straw clogging was almost dissolved due to rotating action with the sharp edge of the new designed blades in front of the furrow openers. Soil cutting depth of strips and seed placement depth was consistent all over the field by the modified blades during wheat and maize sowing. The set of tip angle blades at 15 cm anchored rice residue shown the improved seed germination rate of 95.89% for wheat and 78.65% for maize. The investigation enables scope for adopting modified blade for better performance into CA practice.
Comparison Effect on Biogas Production from Vegetable and Fruit Waste with Rumen Digesta Through Co-Digestion Process
(European Journal of Energy Research, 2022-01-24) Tasnim, Anika; Mamun, Muhammad; Hossen, Md Anwar; Rahman, Towfiq
Biogas is the best renewable energy as it can be produced from any biomass for example any plant or living organism. The purpose of this research was to produce biomethane from co-digestion of vegetable and fruit waste with rumen digesta through anaerobic digestion process. In this research, two trials of experiment were conducted. Each trial has three different sample with different mixing ratios. Raw materials used in the experiment was rumen digesta of goat and cow, potato, capsicum, cucumbers, onions, radish, cauliflower, carrot, leafy vegetables, apple, banana, and papaya. In each sample, 1200 gram of raw materials were used. Hydraulic retention time was 30 days. Data was collected by water displacement method. The experiment found that the gas production started from 2nd or 3rd days and stops in 28th or 29th day. Highest production of biogas was 35, 33, 30, 40, 50 and 35 mL/day on the 17th, 14th, 17th, 11th, 12th and 7th day at the mixing ratios of 1:1:2, 1:2:1, 1:1.5:1.5, 1:0.5:0.5, 1:2:2 and 1.5:1.5:1 (Rumen Digesta: Vegetable Waste: Fruit Waste) respectively. The study suggests making digester for the recycling of waste to produce biogas, a renewable and environment friendly energy.
(Mis)matches in Daily Weight Stigma Perpetrators' and Targets' Genders and Races Relative to Targets' Daily Disordered Eating Behaviors: Examining Differences Between Black and White Women
(Elsevier, 2025) Romano, Kelly; Panza, Emily; Peterson, Carol; Hooper, Laura; Mason, Tyler
Background Associations between weight stigma (WS) and disordered eating behaviors (DEBs) vary based on the WS source (eg, family, strangers). However, no research has examined how (mis)matches in WS perpetrators’ and targets’ races and genders relate to targets’ DEBs in the natural environment (eg, home, work/school). Objective This study examined whether associations between daily WS and DEBs differed: for Black vs White women, based on whether there were (mis)matches in the races and—separately—genders of WS perpetrators and targets. Design This is a secondary analysis of a remote daily diary study conducted in the Mid-Atlantic United States. Participants completed nightly surveys on mobile devices for 14 days from January 2019 to July 2020. Participants/setting Participants included Black (n = 58) and White (n = 86) women with body dissatisfaction (ages 18 to 35 years). Main Outcome Measures Outcomes included binge eating, overeating, loss of control eating, and dietary restriction (skipped meals, refused food/drinks, replaced meals with no/low-calorie substances, and limited food amount). Statistical Analyses Performed Multilevel models examined whether associations between daily WS by men, women, Black or White perpetrators (vs no daily WS; independent variables) and daily DEBs (outcomes) differed for Black vs White women (moderator). Results Associations between daily WS and different DEBs were generally largest for Black women when WS was perpetrated by women and, for White women, by men (with exceptions). For example, on days when Black women experienced WS by other women, they were more likely than White women to engage in binge eating (b = .14 ± .06; P = .024), refuse food/drinks (b = .21 ± .07; P = .004), and limit the amount of food they ate (b = .27 ± .10; P = .008). (Mis)matches in WS perpetrators’ and targets’ races were not associated with Black or White women’s daily DEBs (P values > .05). Conclusions Findings suggest that associations between daily WS and DEBs are especially harmful for women with multiple marginalized identities (Black women), particularly when Black women experience WS by women perpetrators. Further WS research centered on Black women’s experiences is needed.

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