No description available.

No description available.

Dairy cattle make a significant contribution to global methane emissions. Milking cows in the UK make up about a fifth of the total cattle population, with Holstein-Friesian cows being the most common breed. Investigating ways to minimise methane, a potent greenhouse gas (GHG) produced by dairy cows from enteric fermentation and manure, has gained importance in recent years due its role in climate change. Currently, GHG emissions from UK dairy farming are predicted using the Intergovernmental Panel on Climate Change (IPCC) Tier II methodology. The IPCC Tier II methodology and statistical prediction equations from the literature were evaluated for their ability to reliably model methane output using data from the Langhill Holstein-Friesian experimental herd. The Langhill dairy herd is on a long-term breeding and feeding systems experiment, and cows are on average 88% North American Holstein genes. The production systems within the herd represent a range of dairy systems that may be found commercially. Therefore, production values were assumed to be representative of those that could be found in the commercial Holstein-Friesian population, so factors affecting system methane emissions and appropriate mitigation options could be investigated. Prediction equations using dry matter (DM) intake and gross energy intake as input values were the most appropriate equations for reliably estimating daily enteric methane output. However, if DM intake values are not available, the IPCC Tier II method was found to provide a suitable prediction of methane emissions over a cow‘s lactation and lifetime. This study found that GHG emissions from enteric fermentation and manure, expressed as carbon dioxide equivalents (CO2-eq.), account for about 66% of dairy system CO2-eq. emissions, with enteric methane output being the main contributor (34% of system CO2-eq. emissions). Breeding for increased kilograms of milk fat plus protein production was shown to help reduce dairy system methane emissions. Cows of predominantly North American Holstein genes in this study produced more milk when fed a diet with a low proportion of forage and had lower GHG emissions and land requirement per kilogram energy corrected milk than similar cows fed a diet with a higher proportion of forage. Strategies to mitigate GHG emissions (including methane) and the environmental impact of dairy systems should seek to select animals that better utilise their feed intake to meet their genetic potential for milk production.

Aquaculture is one of the fastest growing animal protein production industries and accounted for 47% of the world’s food fish consumption in 2010. Aquaculture production is expected to increase to compensate for projected shortfalls in seafood supply by capture fisheries. Current assessments and scenarios predicting the outcome of this increased production have limited scope and ability to distinguish alternative courses of action. Using the Global Aquaculture Performance Index (GAPI) as a starting point, I have developed an ecologically comprehensive and quantitative farm level assessment. I selected salmon as the candidate to compare production scenarios due to being economically important, data rich and farmed in a diversity of production systems. In applying the farm-level assessment to conventional net-pen salmon production and four alternative systems, I determined the ecological impact per unit of production to be significantly different. It is possible to produce a greater volume of fish for less ecological impact. While there are benefits and trade-offs in the alternative production systems, the results indicate that projected food fish demands can be met in a more sustainable manner. The Farm Level Aquaculture Performance Index (FLAPI) provides a quantitative, performance-based tool that accounts for all ecological impacts and the resulting assessments can be used to benchmark and guide future development of aquaculture. / Graduate / 0792

No description available.

Yes / This paper presents an agent-based framework for reconfiguring modular assembly systems using machine learning and system performance estimates based on previous reconfigurations. During a reconfiguration, system integrators and engineers make changes to the machine to meet new production requirements by increasing capacity or manufacturing new product variants. The framework provides a method for automatically evaluating these changes in terms of impact on the performance of the production system, and building a knowledge base. Such knowledge is used to support future reconfigurations by recommending changes that are likely to improve the performance based on previous reconfigurations. The agent architecture of the framework has two levels, one for individual assembly stations and one for the entire production line. Knowledge bases of changes are built and utilised at both levels using machine learning and performance estimates. A prototype implementation of the proposed framework has been evaluated on an assembly production system in an industrial scenario. Preliminary results show that framework helps to reduce the time and resources required to complete a system reconfiguration and reach the desired production objectives. / This work was supported by the SURE Research Projects Fund of the University of Bradford and the European Commission [grant agreement n. 314762].

Recent developments in the area of Cyber-Physical Production Systems prove that high technology readiness level is already achieved and industrialization of such technologies is not far from today. Although these technologies seem to be convenient in providing solutions to environmental uncertainties, their application provides adaptability only at shop floor level. Needless to say, an enterprise cannot reach true adaptability without ensuring adaptation skills at every level in its hierarchy. Commonly used production planning and control approaches in industry today inherit from planning solutions which are developed in response to historical market characteristics. However, market tendency in recent years is towards making personalized products a norm. The emerging complexity out of this trend obliges planning systems to a transition from non-recurring, static planning into continuous re-planning and re-configuration of systems. Therefore, there is a need of responsive planning solutions which are integrated to highly adaptable production system characteristics. In this dissertation, Demand Responsive Planning, DRP, is presented which is a planning framework aiming to respond to planning needs of shifting trends in both production system technologies and market conditions. The DRP is based on three main constructs such as dynamicity, responsiveness and use of precise data. These features set up the foundation of accomplishing a high degree of adaptability in planning activities. By this means, problems from an extensive scope can be handled with a responsive behavior (i.e. frequent re-planning) by the use of precise data. The use of precise data implies to execute planning activities subject to actual demand information and real-time shop floor data. Within the context of the DRP, both a continuous workload control method and a dynamic capacity adjustment approach are developed. A test-bed is coded in order to simulate proposed method based on a system emulation reflecting the characteristics of cyber-physical production systems at shop floor level. Continuous Precise Workload Control, CPWLC, method is a novel approach aiming at precise control of workload levels with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. As a result, improved shop floor performances are achieved through controlling workload levels precisely by the release of appropriate job types at the right time. Presented dynamic capacity adjustment approach utilizes rapid re-configuration capability of cyber-physical systems in achieving more frequent capacity adjustments. Its implementation architecture is integrated to the CPWLC structure. By this means, a holistic approach is realized whereby improved due date performance is accomplished with minimized shop floor congestion. Hence, sensitivity to changing demand patterns and urgent job completions is improved. / <p>QC 20150907</p>

A single field study was established in 2001 at the Safford Agricultural Center to evaluate a twin-line cotton production system. This location was part of a larger, statewide program conducted in 2001. This location consisted of two separate planting dates (PD) in which two separate planting systems were used. Results from this location indicated trends in yield increases with the twin-line production system when compared to the single or conventional production system. Lint yield increases of approximately 200 lbs. lint/acre were observed on the second PD. Lower yields were observed in the twin-line planting with the first PD which was in part due to poor seed placement with the equipment used to plant the twin-line on the first PD. Results indicate the potential for increased yield with the twin-line production system with the caveat that the proper equipment be used to plant the twin-line system to ensure precise and consistent seed placement and spacing.

Three experiments were conducted in Maricopa, Marana, and Glendale, Arizona in 2001 to measure cotton growth, yield, micronaire, and production costs in single and double seed line per bed systems on 32 and 40 inch beds. Canopy development was faster and canopy closure was greater in the double seed line than in the single seed line systems and was greater in the 32 inch than in the 40 inch row systems. At Maricopa, the single line 32 inch system yield of 1571 lbs./A was significantly greater than the yields of the other seed line/row spacing systems. The yields of the single line 40 and the double line 32 inch systems were not significantly different at 1476 and 1411 lbs. of lint/A, respectively, and the yields of the double line 32 and the double line 40 inch systems also were not significantly different at 1411 and 1396 lbs. of lint/A, respectively. There were no significant lint yield differences at the Marana or Glendale location. At Marana, the lint yields were 1063 and 1066 lbs./A for the single and double seed line 40 inch row spacing systems, respectively. At Glendale, the single and double seed line 38 inch row spacing systems yielded 1474 and 1551 lbs. of lint/A, respectively. In all 2001 experiments, there was a trend for reduced micronaire in the double seed line per bed systems compared to the single seed line per bed systems. At Maricopa, the average micronaire was 5.0 and 4.7 for the single and double seed line per bed 32 inch row system, respectively, and 5.2 and 4.9 for the single and double seed line per bed 40 inch row systems, respectively. At Marana, the micronaire was 4.7 and 4.5 for the single and double seed line per bed 40 inch row systems, respectively. At Glendale, the micronaire was 5.1 and 4.6 for the single and double seed line per bed 38 inch row systems, respectively. Production costs were similar for the single and double seed line per bed systems. Additional research will be conducted in 2002 to determine the optimum plant populations and in-row plant spacings for double seed line per bed production systems.

Three experiments at the University of Arizona Maricopa and Marana Agricultural Centers in 2002 and 2003 measured effect of plant populations on the yield of cotton planted in the twin seed-line per bed configuration. In 2002 at the Maricopa Ag. Center, the plant populations were 52800, 69200, 82800 and 96200 plants per acre (PPA) for Stoneville 4892BR and 54800, 70800, 90500 and 104500 PPA for AG3601, respectively. The two lowest plant populations which were in the range of common commercial plant densities resulted in the greatest lint yields for both varieties (an average of 1708 and 1287 lb lint/A for ST4892BR and AG3601, respectively) but there was a significant linear decrease in yield with increasing plant population. In 2003, the cotton variety Delta Pine 449BR was planted and the population densities were 22000, 29000, 36000, 46000, 51000, 61000, and 64,000 PPA at the Marana Ag. Center and 24000, 34000, 41000, 56000, 63000, 71000, and 86,000 PPA at the Maricopa Ag. Center. Cotton yield did not vary significantly as a function of population density at Maricopa and averaged 1526 lb lint/A. At Marana there was a slight trend of increasing yield with increasing plant densities with the three highest plant populations averaging 1385 lb lint/A. In the experiments with ST4892BR and AG3601 at Maricopa in 2002 and with DP449BR in 2003 there was a linear decrease in fiber micronaire with increasing density but this effect of density on micronaire was not observed possibly because plant populations Marana were lower than in the other experiments.