Identified online via keyword search of UI eCatalog, Examines the inter-relationship between journalism and poetry in the 19th century, when poems on topical issues regularly appeared in newspapers alongside prose reports of the same events.
Wallander, Steven (author), Smith, David (author), Bowman, Maria (author), and Claassen, Roger (author)
Format:
Research report
Publication Date:
2021-02
Published:
USA: Economic Research Service, U.S. Department of Agriculture
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 202 Document Number: D12143
Notes:
Economic Information Bulletin Number 222. 33 pages., This report detailed how cover crops are managed on corn, cotton, soybean, and wheat fields in the United States. "These surveys reveal that there are many different approaches to using cover crops."
Bin, Li (author), Shahzad, Muhammad (author), Khan, Hira (author), Bashir, Muhammad Mehran (author), Ullah, Arif (author), and Siddique, Muhammad (author)
Format:
Journal article
Publication Date:
2023-09-18
Published:
Switzerland: MDPI
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 206 Document Number: D12959
20 pages, Sustainable agriculture is a pivotal driver of a nation’s economic growth, especially considering the challenge of providing food for the world’s expanding population. Agriculture remains a cornerstone of many nations’ economies, so the need for intelligent, sustainable farming practices has never been greater. Agricultural industries worldwide require sophisticated systems that empower farmers to manage their crops efficiently, reduce water wastage, and optimize yield quality. Yearly, substantial crop losses occur due to unpredictable environmental changes, with improper irrigation practices being a leading cause. In this paper, we introduce an innovative irrigation time control system for smart farming. This system leverages fuzzy logic to regulate the timing of irrigation in cotton crop fields, effectively curbing water wastage while ensuring that crops receive neither too little nor too much water. Additionally, our system addresses a common agricultural challenge: whitefly infestations. Users can adjust climatic parameters, such as temperature and humidity, through our system, which minimizes both whitefly populations and water consumption. We have developed a portable measurement technology that includes air humidity sensors, temperature sensors, and rain sensors. These sensors interface with an Arduino platform, allowing real-time climate data collection. This collected climate data is then sent to the fuzzy logic control system, which dynamically adjusts irrigation timing in response to changing environmental conditions. Our system incorporates an algorithm that generates highly effective (IF-THEN) fuzzy logic rules, significantly improving irrigation efficiency by reducing overall irrigation duration. By automating the irrigation process and precisely delivering the right amount of water, our system eliminates the need for human intervention, rendering the agricultural system more dependable in achieving successful crop yields. Water supply commences when the environmental conditions reach specific thresholds and halts when the requisite climate conditions are met, maintaining an optimal environment for crop growth.
12 pages, We use the 2013 cotton precision farming survey data to study the adoption of irrigation technologies by cotton farmers in 14 states of the United States. We find that farmers with a higher irrigated yield, and who are from the Southern Plains (Texas and Oklahoma), adopt water-efficient irrigation technologies, such as sub-surface drip and trickle irrigation technologies. There are 10 irrigation technologies that farmers can adopt for cotton production in these 14 cotton-growing states. The intensity of the irrigation technologies, as measured by the number of irrigation technologies adopted in cotton production, is affected by the irrigated cotton yield realized, land holding (total land owned), education, computer use, and the origin of the cotton farmer being from the Southern Plains. We use a multivariate fractional regression model to identify land allocation by the different irrigation technologies used. Our results indicate that significant variables affecting land allocation with different irrigation technologies are the age of the operator, the cover crop, the information sources used, the per acre irrigated yield, the education, and the cotton farmer being from the Southern Plains.
23 pages, Cotton is the most significant natural fiber in the world and an important part of the global economy. Yet, the cotton industry faces several challenges in securing its place in the global fiber market share, reaching new consumers, and maintaining relationships with current consumers. Furthermore, the cotton industry has a unique opportunity to share evidence-based information with followers through its product marketing on social media. The study described herein used content analysis to explore Instagram content on the @discovercotton profile. Content included categories of promoted products (i.e. women, men, children, or home); comments, posts, and caption stimuli; and most frequently used word, hashtag, and retail partner stimuli. We analyzed 434 Instagram stimuli (244 single photos, 142 carousels, and 48 videos) from March 2, 2021, to March 2, 2023. Across all stimuli, there were 110,143 likes and 5,799 comments with total response (engagement: likes and comments) reaching 115,942. We found that women’s products were promoted most often followed by men, home, and children—only 8.48% of stimuli depicted cotton, a cotton plant, or the seal of cotton. We identified six major themes in caption stimuli on @discovercotton: qualities of cotton, style, sustainability, check the label, women, and cotton production. Cotton was the most frequently used word stimuli in captions, and cotton as a fabric was the most promoted theme.
30 pages, While Georgia is one of the top producers of cotton and peanuts in the Unites States, much attention has been centered on ensuring sustainability in the production of these crops. The need to understand what drives farmers’ decision to utilize voluntary best management practices is critical for the improvement of strategies focused on increasing farmers’ adoption of these practices. Empirical evidence that identifies influential factors in farmers’ decision to adopt best management practices have yet to produce consistent predictors of adoption behavior. This has led to increased calls for unique approaches examining how farmers’ views and motivations impact their adoption decisions regarding these practices. This study builds on previous research using Q methodology to provide an in-depth investigation of the differences in farmers’ views on best management practices for cotton and peanut production in southwest Georgia. A purposive sample of 21 participants completed the Q sorting exercise and semi-structured interviews. Analysis of the data revealed three primary viewpoints that were identified and labeled as the: (1) land preservers, (2) ambitious self-starters, and (3) principled go-getters. These perspectives show the differences in motivations for adoption behavior and denote the range of influential factors on farm management decisions. The findings of this study reveal key viewpoints held by Georgia cotton and peanut farmers toward best management practices, which can inform the development of strategically tailored educational resources and opportunities. These targeted educational approaches must account for the specific needs and preferences of farmers found in this study to potentially increase adoption.
