Forage Yield Variability on New York Dairy Farms
| dc.contributor.author | Long, Emmaline Anne | |
| dc.contributor.chair | Ketterings, Quirine M | |
| dc.contributor.committeeMember | DeGloria, Stephen D | |
| dc.date.accessioned | 2017-07-07T12:48:51Z | |
| dc.date.available | 2017-07-07T12:48:51Z | |
| dc.date.issued | 2017-05-30 | |
| dc.description.abstract | Dairy farms can improve their environmental footprint by feeding more homegrown forage. As a consequence higher yields will reduce feed imports and enhance nutrient use efficiency. To improve forage production, limitations to production need to be identified. While yield records can provide integral information, whole farm evaluations have shown that accurate yield data are difficult to collect for alfalfa (Medicago sativa L.) and grass mixtures and corn (Zea mays L.) silage fields. In particular, there is a need for long-term yield records to evaluate yield stability and production trends. Such information should allow for identification of the system with the best biological buffering capacity (resilience) under changing climate conditions. Additionally, on-farm research, a recommended tool for adaptive management, can benefit from practical ways to collect yield data. Recently, forage yield monitors have become available on self-propelled forage harvesters (SPFHs), but precision and accuracy of this technology are unknown. Three studies were conducted. In the first study, accuracy of yield and moisture sensing components of forage yield monitors were evaluated for use in alfalfa/grass and corn silage. Moisture content, mass flow weights, total area harvested and total dry yield per hectare were measured on 11 farms in 2013; forage samples were collected for truck loads, analyzed for dry matter content, and compared to monitor-registered dry matter. Truck weights were used to compare monitor-derived yield to actual yield on two farms for alfalfa/grass and three farms for corn silage. It was concluded that when calibrations are done regularly, forage yield monitors can provide an accurate and precise measure of dry yield for adaptive management. This technology can be used when plots are large and large treatment-driven yield differences are expected. In the second study, 14 years of yield data from a 1000-cow dairy farm were analyzed. Individual field yield and farm-average yields of corn silage and alfalfa, and grass hay mixtures was measured. Fields were classified in four quadrants based on yield and yield variability over time. Soil physical and chemical properties were evaluated as potential indicators of biological buffering capacity. Across all fields, corn silage yield increased from 13.3 to 17.8 Mg DM ha-1 between 2000 and 2013 whereas hay yield averaged 8.6 Mg DM ha-1 without a trend. It was concluded that management practices that increase organic matter, improve drainage, and provide optimal soil fertility will result in higher and more stable yields that are less impacted by weather extremes. In the third study, field variability of corn silage across a range of farms and fields was quantified using 3-4 year corn silage yield data. Within-field variability was then evaluated as impacted by the overall yield level and yield stability for the field. Understanding how within-field variability is impact by yield and yield stability will aid in deciding when to invest in precision agriculture technologies. | |
| dc.identifier.doi | https://doi.org/10.7298/X4X34VK5 | |
| dc.identifier.other | Long_cornell_0058O_10104 | |
| dc.identifier.other | http://dissertations.umi.com/cornell:10104 | |
| dc.identifier.other | bibid: 9948910 | |
| dc.identifier.uri | https://hdl.handle.net/1813/51685 | |
| dc.language.iso | en_US | |
| dc.subject | Agronomy | |
| dc.subject | alfalfa | |
| dc.subject | corn silage | |
| dc.subject | precision agriculture | |
| dc.subject | self propelled forage harvester | |
| dc.subject | yield monitor | |
| dc.title | Forage Yield Variability on New York Dairy Farms | |
| dc.type | dissertation or thesis | |
| dcterms.license | https://hdl.handle.net/1813/59810 | |
| thesis.degree.discipline | Animal Science | |
| thesis.degree.grantor | Cornell University | |
| thesis.degree.level | Master of Science | |
| thesis.degree.name | M.S., Animal Science |
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