A structured management approach to implementation of health promotion interventions in Head Start.

Improving the health and health literacy of low-income families is a national public health priority in the United States. The federal Head Start program provides a national infrastructure for implementation of health promotion interventions for young children and their families. The Health Care Institute (HCI) at the Anderson School of Management at the University of California, Los Angeles, developed a structured approach to health promotion training for Head Start grantees using business management principles. This article describes the HCI approach and provides examples of implemented programs and selected outcomes, including knowledge and behavior changes among Head Start staff and families. This prevention-focused training platform has reached 60,000 Head Start families in the United States since its inception in 2001. HCI has demonstrated consistent outcomes in diverse settings and cultures, suggesting both scalability and sustainability

Application of Fall Nitrogen Increased Spring Tall Fescue Yield

Late season nitrogen fertilization is a practice that has long been implemented in turfgrass production. This practice involves applying nitrogen from September through December and results in an extended green period in the fall without stimulating excessive shoot growth. The longer green period results in higher levels of carbohydrate reserves and enhanced root growth compared with a spring-summer fertilization program. In addition to these benefits, sods fertilized in late fall have been shown to green up two to six weeks earlier in the spring. Limited research has considered the impact of late fall nitrogen applications on grass growth in hayfields and pastures. The objective of this study was to evaluate the impact of late fall nitrogen fertilization on dry matter production of an established tall fescue stand. In 2020 and 2021, the study was conducted at the UK Research and Extension Center located in Princeton, KY. The experimental design was a random complete block with four replications. In early December 2020 and 2021, 0, 34, 67, and 101 kg ha-1 was applied as ammonium nitrate, ammonium sulfate, and Anuvia SYMTRX 20S. An additional 90 kg ha-1 was applied as urea to all plots the following spring. Plots were harvested on 13-May-2021 and 24-May-2022 using a self-propelled sickle bar type forage harvester equipped with load cells. Averaged across sources, first harvest yields ranged from 3428 to 5675 and 3929 to 5831 kg ha-1 in 2021 and 2022, respectively. Fall applied nitrogen resulted in a linear increase in dry matter yield of 25.0 and 21.2 kg ha-1 in 2021 and 2022, respectively. Nitrogen sources had no impact on dry matter yield (P \u3e 0.05). Plots receiving fall applied nitrogen also initiated growth earlier in the spring

Impact of Brown Midrib Trait on the Decomposition Rate of Sorghum-Sudangrass Residue in Pastures

Sorghum-sudangrass (Sorghum bicolor var. bicolor x bicolor var. sudanense) can provide high quality summer grazing. Some varieties possess the brown midrib (BMR) trait which results in reduced lignin resulting in higher digestibility and animal performance. If microbes in the rumen can digest BMR sorghum-sudangrass more completely, then soil macro/micro flora and fauna may do so as well. This could result in nutrients being returned to the soil faster from plants containing the BMR trait. The objective of this study was to determine the decomposition rate of BMR and non-BMR sorghumsudangrass. The experimental design was a random complete with four replications. Sorghum-sudangrass with and without the BMR trait was placed in litter decomposition bags as whole plants or divided into leaves and stems. A composite sample was taken when bags were loaded to determine initial dry matter. Loaded bags were then placed on the soil surface in a pasture and collected at 1, 2, 3, 4, 6, 8, 10, 12, 14, or 16 weeks after placement. Upon collection plant material was dried for 3-days at 55°C in a forced air oven. Dry weights at each collection date were subtracted from the initial dry weight to determine total DM loss. The BMR trait did not impact dry matter loss in the leaves. Stems possessing the BMR trait lost dry matter at a greater rate resulting in dry matter losses at 14 weeks of 78 and 68% and 59 and 47% for the BMR and non-BMR varieties in trials 1 and 2, respectively. Whole plants showed limited differences in dry matter loss at 14 weeks after placement

Improving Frost Seeding Accuracy with an Entry Level GPS Unit

Guidance utilizing GPS has long been used for various operations in row crop agriculture. However, the high cost of these systems has limited their use in low-input forage and livestock operations. Reduced prices and the availability of used guidance systems have the potential to increase the use of precision agriculture in pastoral settings. In the past, frost seeding often resulted in areas that received no seed and areas that were double seeded. The objective of this experiment was to evaluate the impact of using a guidance system on the uniformity of seed dispersal. This study was conducted at the University of Kentucky’s Research and Education Center, located in Princeton, KY, USA in 2019 and 2021. The experimental design was a randomized complete block with four replications. Four pastures ranging from 2.5 to 4.3 ha were mock seeded using a UTV equipped with GPS guidance technology. The guidance system was initiated, but covered with an opaque bag, and the four pastures were driven by sight alone. This mock seeding process was then repeated utilizing the guidance system. Frost seeding without GPS guidance resulted in a 49% and 21% overlap in 2019 and 2021, respectively. At an overseeding cost of $89/ha and an average overlap of 35%, the cost of a guidance system could be recouped in as little as 48 ha. The results of this study indicate that GPS guidance systems have the potential to improve the uniformity of seed dispersal, thus reducing the cost of frost seeding for producers

Ambitious partnership needed for reliable climate prediction.

Current global climate models struggle to represent precipitation and related extreme events, with serious implications for the physical evidence base to support climate actions. A leap to kilometre-scale models could overcome this shortcoming but requires collaboration on an unprecedented scale