Cool-Season Perennials and Stability in Year-Round Forage Production Systems

Changes in long-term climate normals have resulted in warmer and wetter summers and milder winters in the humid eastern United States. This will likely impact regional forage species adaptation in the long-term and varietal adaptation in the short term. Variety evaluation has been occurring at the University of Kentucky for almost 100 years. There are several considerations for selecting forage species and varieties including regional and local adaptation, productivity, distribution of growth, palatability, nutritive value, anti-quality factors, tolerance to stress, and persistence. Two of the most important criteria are long-term productivity and persistence under grazing, both of which are currently being evaluated in Kentucky. One potential way to use long-term data to aid in the selection of resilient cool-season perennial grass varieties for year-round grazing systems may be to graph yield (x-axis) against persistence (y-axis) where ‘100’ represents the average for the trials. This allows varieties to be ranked either above or below average for yield and persistence. Varieties in the upper right-hand quadrant are varieties that have above-average yield and persistence and would be good candidates to include in year-round grazing systems. In contrast, varieties in the lower left-hand quadrant are varieties that are below average in both yield and persistence and probably are not good candidates to include in a year-round grazing system. This approach may require adapting current variety testing strategies to better assess yield potential and persistence under grazing

Impact of Fertilizer Type, Seeding Coating, and Duration of Exposure on the Germination of Red Clover Seed

Legumes are important components in grassland ecosystems. Red clover is one the most used legumes in the transition zones states like Kentucky. To maintain legumes in grass pastures, improved red clover varieties are often overseeded in the late-winter or early-spring. In many cases seed is mixed with fertilizer and top-dressed onto pastures. Little data are available on the impact of fertilizer type or duration of exposure on the germination of raw and coated red clover seed. The objective of this study was to evaluate the impact of two fertilizer types, muriate of potash and a blended fertilizer (urea, diammonium phosphate, and muriate of potash), and the duration of exposure (1 to 28 days) on the germination of an improved red clover variety that was raw or coated. Mixing seed with the blended fertilizer resulted in a linear decrease in germination rate for the raw seed and quadratic decrease for the coated seed. After 20 days of exposure to the blended fertilizer, the germination rate of the coated and raw seed was 0 and 60%, respectively. Combining seed with muriate of potash resulted in a linear decline in germination rate with the decline being greater for the coated seed. Overall, the rate of decrease was considerably less than that of the blended fertilizer. Results of this study indicate that seed coating enhanced the detrimental effects of fertilizer on seed germination

Arithmetic complexity via effective names for random sequences

We investigate enumerability properties for classes of sets which permit recursive, lexicographically increasing approximations, or left-r.e. sets. In addition to pinpointing the complexity of left-r.e. Martin-L\"{o}f, computably, Schnorr, and Kurtz random sets, weakly 1-generics and their complementary classes, we find that there exist characterizations of the third and fourth levels of the arithmetic hierarchy purely in terms of these notions. More generally, there exists an equivalence between arithmetic complexity and existence of numberings for classes of left-r.e. sets with shift-persistent elements. While some classes (such as Martin-L\"{o}f randoms and Kurtz non-randoms) have left-r.e. numberings, there is no canonical, or acceptable, left-r.e. numbering for any class of left-r.e. randoms. Finally, we note some fundamental differences between left-r.e. numberings for sets and reals

Green Canopy Cover Percentage as a Method for Quantifying \u3ci\u3eAndropogon virginicus\u3c/i\u3e (Broomsedge) Reduction through Fertilizer Applications in a Cool Season Hay Production System

Remote sensing has been used to measure green canopy cover for a variety of agronomic purposes. This study explores the use of digital imagery as a method to quantify warm and cool season grasses in a hay production system. Due to alternate growth periods, cool and warm season grasses show greener color in different seasons. These seasonal color shifts provide an opportunity to measure their respective percentages when growing together in a system. This study was conducted in a hay field that was originally dominated by cool season grasses including tall fescue (Festuca arundinacea) and Kentucky bluegrass (Poa pratensis), but had been invaded by broomsedge (Andropogon virginicus), a native, warm season grass that is known to be an indicator of poor soil fertility. Soil tests for this field revealed a severe potash deficiency. Plots with high percentages of broomsedge would only be greener during the peak times for warm season grasses, and have a lower percentage of green canopy cover throughout the rest of the year, leading to a lower average green canopy cover percentage for the entire year. Thirteen different fertilizer regimens were applied with four replications each in a randomized complete block design (N-P-K kg/ha: 0-0-0, 0-0-202, 0-0-404, 0-45-0, 0-45-202, 0-45- 404, 202-0-0, 202-0-202, 202-0-404, 202-45-0, 202-45-202, 202-45-404, 43-43-43). Images were captured with a Nikon D750 DSLR camera and a DJI Phantom 4 Pro V2.0 drone throughout 2020, and analyzed for green canopy cover percentage using the Canopeo application in MATLABR2020b.The two fertilizer treatments showing the highest yearly averages for green canopy cover were those that contained the highest levels of N, P, and K (202-45-202 kg/ha and 202-45-404 kg/ha). This method of collecting data was both time and labor efficient and yielded adequate and useful data

Drone and Digital Camera Imagery Estimate C3 and C4 Grass Ratios in Pastures

The following study investigates the accuracy and practicality of exploiting the color dichotomy present between C3 and C4 grass species to estimate their respective proportions from drone or camera captured imagery. Understanding the proportions of C3 and C4 grasses in pastures is vital to sound decision making for livestock production. The ability to monitor these proportions remotely will also allow for large scale monitoring as well as detection of changes in botanical composition over time and in response to weather events, management, or climate change. A free green canopy cover (GCC) analyzing software, Canopeo, was used to quantify green plants in captured images, providing an estimation of C3 grasses that retain green color in colder seasons while C4 grasses do not. The GCC estimates from Canopeo were compared to what was measured using occupancy grids. We found that green canopy cover software could estimate the proportion of C3 grasses in images captured by a drone and a Nikon camera

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