4 pages, via Online journal, Bougainvillea (Bougainvillea sp.) plant inflorescence number will vary in response to multiple cues such as changes in temperature, water, light intensity, pruning, and photoperiod. Previous research reports that the application of plant growth regulators (PGRs) to bougainvillea grown under varying photoperiods improved inflorescence number, probably as a result of changes in gibberellic acid (GA) levels. There are many bioactive plant GAs, but we chose to investigate differences in gibberellic acid 3 (GA3) levels and inflorescence number in response to the application of ethephon (2-cholorethylphosponic acid) or abscisic acid (ABA) to ‘Afterglow’ bougainvillea (Bougainvillea ×buttiana) grown under 14-hour photoperiod [long-day (LD)] conditions. Plants were 5 inches tall with seven visible lateral nodes and were grown in a greenhouse in 4-inch pots filled with 5-mm coarse aquarium zeolite. Ethephon was applied as a foliar spray at 0.05, 0.07, 0.10, 0.15, or 0.20 mg/plant. ABA was applied as a soil drench at 1, 1.5, 3, 6, 8, or 10 mg/plant. Endogenous levels of GA3 were measured 1 and 48 days after treatment to calculate the change in GA3 (∆GA3). A short day (SD) control of 8 hours was included to measure differences in inflorescence number and ∆GA3 between photoperiods. ‘Afterglow’ plants grown under SD conditions had the greatest decrease in ∆GA3 (–1.09 µg·g–1) over 48 days and the most inflorescences (10.6) compared with LD control plants with a decrease in ∆GA3 of –0.09 µg·g–1 and fewer inflorescences (1.0). Plants grown under LD conditions and treated with 0.05 mg/plant ethephon had inflorescence numbers (9.6) and levels of ∆GA3 (–0.74 µg·g–1) similar to the SD control. As ethephon rate increased to more than 0.05 mg/plant, inflorescence number on LD plants decreased and ∆GA3 increased. Exogenous ABA rates of 1 mg/plant produced inflorescence numbers (1.4) and ∆GA3 (–0.10 µg·g–1) similar to the LD control. As the rate increased, ∆GA3 increased and inflorescence number decreased. Plants treated with ABA rates of 3 mg/plant and more were defoliated and had no inflorescences.
11 pages, via Online journal, The Soil Vulnerability Index (SVI) was developed by the USDA Natural Resources Conservation Service (NRCS) to identify inherent vulnerability of cropland to runoff and leaching. It is a simple index that relies on the SSURGO database and can be used with basic knowledge of ArcGIS. The goal of this study was to investigate a relationship between constituent (sediment and nutrient) loadings and fraction of the watershed in each SVI class. The SVI maps were developed for each of the seven subwatersheds of the Mark Twain Lake watershed in Missouri, which were similar in soil conditions and climatic variability. The SVI assessment was performed by investigating if the distribution of the SVI for cropland in each subwatershed could help explain measured 2006 to 2010 sediment and nutrient loads better than crop distribution alone. Regression analyses were performed between annual loads of sediment and nutrients exported from the watersheds and a composite number that included either cropland distribution alone, or cropland distribution combined with the SVI. Coefficients of determination and p-values were compared to assess the ability of land use and SVI distributions to explain stream loads. Integrating the SVI in the land cover variable improved the ability to explain constituent loads in the watersheds for sediment, total nutrients, and dissolved nitrogen (N). Regression results with and without the SVI were identical for dissolved phosphorus (P), potentially indicating that SVI was not indicative of dissolved P transport at the current site. Overall, the application of the SVI at watershed scale was not perfect, but acceptable at correctly identifying cropland of greatest vulnerability and linking with transported constituent loads.
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 202 Document Number: D12151
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Online via AgriMarketing Weekly. 1 page., Data, analytics and technology company DTN acquires a global farm-level data source, Farm Market ID. Both share goals of helping agribusinesses support producers.
10 pages, Enormous quantities of data are generated through social and online media in the era of Web 2.0. Understanding consumer perceptions or demand efficiently and cost effectively remains a focus for economists, retailer/consumer sciences, and production industries. Most of the efforts to understand demand for food products rely on reports of past market performance along with survey data. Given the movement of content-generation online to lay users via social media, the potential to capture market-influencing shifts in sentiment exists in online data. This analysis presents a novel approach to studying consumer perceptions of production system attributes using eggs and laying hen housing, which have received significant attention in recent years. The housing systems cage-free and free-range had the greatest number of online hits in the searches conducted, compared with the other laying hen housing types. Less online discussion surrounded enriched cages, which were found by other methods/researchers to meet many key consumer preferences. These results, in conjunction with insights into net sentiment and words associated with different laying hen housing in online and social media, exemplify how social media listening may complement traditional methods to inform decision-makers regarding agribusiness marketing, food systems, management, and regulation. Employing web-derived data for decision-making within agrifood firms offers the opportunity for actionable insights tailored to individual businesses or products.
7 pgs, Extension is uniquely positioned to deliver data-driven solutions to complex community issues with University applied research, particularly through crises like COVID-19. Applying the Policy, Systems and Environmental (PSE) framework to community development is an effective, innovative approach in guiding Extension leaders to create, document, and share long-term transformative change on challenging issues with stakeholders. Beyond the public health sector, applying a PSE approach to community development provides leverage points for population-level benefits across sectors. This article describes current public health approaches, methodologies, and how the PSE framework translates to other programs with four examples of high-impact, systems level Extension projects.
28pgs, Technological advancement is seen as one way of sustainably intensifying agriculture. Scholars argue that innovation needs to be responsible, but it is difficult to anticipate the consequences of the ‘fourth agricultural revolution’ without a clear sense of which technologies are included and excluded. The major aims of this article were to investigate which technologies are being associated with the fourth agricultural revolution, as well as to understand how this revolution is being perceived, whether positive or negative consequences are given equal attention, and what type of impacts are anticipated. To this end, we undertook a content analysis of UK media and policy documents alongside interviews of farmers and advisers. We found that the fourth agricultural revolution is associated with emergent, game-changing technologies, at least in media and policy documents. In these sources, the benefits to productivity and the environment were prioritised with less attention to social consequences, but impacts were overwhelmingly presented positively. Farmers and advisers experienced many benefits of technologies and some predicted higher-tech futures. It was clear, however, that technologies create a number of negative consequences. We reflect on these findings and provide advice to policy-makers about how to interrogate the benefits, opportunities, and risks afforded by agricultural technologies.
8pgs, The agribusiness sector includes a diverse group of interests - crop producers, livestock and meat producers, poultry and egg companies, dairy farmers, timber producers, tobacco companies and food manufacturers and stores. The industry has new-found relevance going into 2019 as the trade war between China and the United States continues to rage leaving many in the business, especially soybean farmers, hurting.
The industry's giving reached its peak in the 2016 presidential cycle spending more than $118 million. The number fell in 2018 to more than $92 million, but was good for the third-highest spending cycle, and highest for a midterm, the industry has had.
17 pages, Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be useful, must be transformed into actionable knowledge. Approaches based on Information Communication Technologies (ICT) have been shown to be able to help farmers and related stakeholders make decisions on problems by examining large volumes of data while assessing multiple criteria. In this paper, we address the automated identification (and count the instances) of the major threat of olive trees and their fruit, the Bactrocera Oleae (a.k.a. Dacus) based on images of the commonly used McPhail trap’s contents. Accordingly, we introduce the “Dacus Image Recognition Toolkit” (DIRT), a collection of publicly available data, programming code samples and web-services focused at supporting research aiming at the management the Dacus as well as extensive experimentation on the capability of the proposed dataset in identifying Dacuses using Deep Learning methods. Experimental results indicated performance accuracy (mAP) of 91.52% in identifying Dacuses in trap images featuring various pests. Moreover, the results also indicated a trade-off between image attributes affecting detail, file size and complexity of approaches and mAP performance that can be selectively used to better tackle the needs of each usage scenario.
11 pages, This proof-of-concept study explores the innovative application of Large Language Models (LLMs) for qualitative analysis of feedback from an Extension program, addressing the challenge of efficiently analyzing qualitative data. The study juxtaposes traditional human-led qualitative analysis with Artificial Intelligence (AI)-driven techniques, revealing the complementary strengths of human insights and AI efficiency. It underscores the potential of LLMs to enhance qualitative analysis while recognizing the need for human oversight to ensure depth and context accuracy. This research contributes to the fields of program evaluation and data analysis, offering a new paradigm for integrating advanced AI tools in qualitative research.
