Resource selection by grazing herbivores on post-fire regrowth in a West African woodland savanna

2007 ◽  
Vol 34 (2) ◽  
pp. 77 ◽  
Author(s):  
Erik Klop ◽  
Janneke van Goethem ◽  
Hans H. de Iongh

The preference of grazing herbivores to feed on grass regrowth following savanna fires rather than on unburnt grass swards is widely recognised. However, there is little information on which factors govern patterns of resource selection within burnt areas. In this study, we attempted to disentangle the effects of different habitat and grass sward characteristics on the utilisation of post-fire regrowth by nine species of ungulates in a fire-dominated woodland savanna in north Cameroon. We used resource-selection functions based on logistic regression. Overall, the resource-selection functions identified the time elapsed since burning as the most influential parameter in determining probability of use by ungulates, as most species strongly selected swards that were recently burned. This pattern might be related to nutrient levels in the grass sward. In addition, most species selected areas with high grass cover and avoided grass swards with high amounts of dead stem material. This is likely to increase bite mass and, hence, intake rates. The avoidance of high tree cover by some species may suggest selection for open areas with good visibility and, hence, reduced risk of predation. Body mass seemed to have no effect on differential selection of post-fire regrowth, irrespective of feeding style.

2001 ◽  
Vol 09 (4) ◽  
pp. 352-358
Author(s):  
Lu Baozhong ◽  
Zhijun Ma ◽  
LI Xin-Hai ◽  
LI Dian-Mo ◽  
DING Chang-Qing ◽  
...  

2021 ◽  
Vol 49 (4) ◽  
pp. 806-816
Author(s):  
Paulo Ávila ◽  
António Pires ◽  
Goran Putnik ◽  
João Bastos ◽  
Maria Cruz-Cunha

The selection of the resources system (SRS) is an important element in the integration/project of Agile/Virtual Enterprises (A/V E) because its performance is dependent of this selection, and even of its creation. However, it remains a difficult matter to solve because is still a very complex and uncertain problem. We propose that using Value Analysis (VA) in the pre-selection of resources phase represents a significant improvement of the SRS process. The current literature fails to formally address the pre-selection phase and none of the resource selection models incorporate the resources value and its consequence in the complexity of the selection process. Whereby, ours developed model with VA constitutes an innovative approach towards greater sustainability in the configuration of A/V E in the context of Industry 4.0, where a massive interconnection among enterprises is expected and consequently the increase of the selection process complexity. After the construction of a demonstrator tool for a set of the problem formulations, this paper verifies by computational results the thesis regarding the benefits of applying VA to the SRS process: VA reduces the complexity of the SRS process, even ensuring that the final system of resources achieve higher quality/value grade.


2021 ◽  
Author(s):  
Paul Laris ◽  
Moussa Koné ◽  
Fadiala Dembélé ◽  
Lilian Yang ◽  
Rebecca Jacobs

Abstract. Savanna fires contribute significantly to greenhouse gas emissions. While it is recognized that these fires play an important role in the global methane cycle, there are too few accurate estimates of emissions from West Africa, the continent's most active fire region. Most estimates of methane emissions contain high levels of uncertainty because they are based on generalizations of diverse landscapes that are burned by complex fire regimes. To improve estimates we used an approach grounded in the burning practices of people who set fires to working landscapes. We conducted 97 experimental fires collecting data for savanna type, grass type, biomass composition and amount consumed, scorch height, speed of fire front, fire type and ambient air conditions for two sites in Mali. We collected smoke samples for 36 fires using a canister method. We report values for fire intensity, combustion completeness, patchiness, modified combustion efficiency (MCE) and emission factor (EF). Our study finds that methane EFs ranged from 3.71 g/kg in the early dry season (EDS) to 2.86 in the mid-dry season (MDS). We found head fires had nearly double the CH4 EF of backfires (4.89 g/kg to 2.92). Fires during the MDS have the lowest intensity values and the lowest methane emissions 0.981 g/m2 compared with 1.030 g/m2 for EDS and 1.102 g/m2 for the late dry season (LDS). We conclude that policies aimed at shifting the burning regime earlier to reduce methane emissions will not have the desired effects, especially if fire type is not considered. We recommend using the adjusted mean value of 0.862 g/m2—based on the carbon content for West African grasses—for calculating emissions for West African savannas.


In the Indian scenario construction industry facing a major problem is cost and time overrun. Effective time performance and cost performance are very important to execute the project in a successful manner by keeping them within the prescribed schedule and cost. Overall cost and duration of construction projects affected by the effective resource selection factor. This paper's objective is to rectify the improper selection of resources by a programming tool. Field survey and codebook study did collect the needed data to feed in the programming tool. The prepared tool gets distributed and making to access by every stakeholder of construction projects. This may result in the selection of construction resources as effectively. The term cost overrun in the resource part will be reduced.


Cloud computing allows users to use resources pay per use model by the help of internet. Users are able to do computation dynamically from different location by using internet resources. The major challenging task in cloud computing is efficient selection of resources for the tasks submitted by users. A number of heuristics and meta-heuristics algorithms are designed by different researchers. The most critical phase is the selection of appropriate resource and its management. The selection of resource include to identify list of authenticated available resources in the cloud for job submission and to choose the best resource. The best resource selection is done by the analysis of several factors like expected time to execute a task by user, access restriction to resources, and expected cost to use resources. In this paper, cloud architecture for resource selection is proposed which combines these factors and make the effective resource selection. In this paper a modified flower pollination algorithm is proposed to migrate the task on efficient virtual machine. The selection of the efficient virtual machine is calculated by the fitness function. By calculating the fitness function, the modified FPA algorithm is used to take the decision regarding VM migration is required to improve the resource efficiency or not. In this paper Virtual machine mapper maps the task as per knowledge base i.e. past history of the virtual machine, task type whether computational or communicational based. The results are compared with the existing meta-heuristic algorithms.


2015 ◽  
Vol 305 ◽  
pp. 10-21 ◽  
Author(s):  
Michel P. Laforge ◽  
Eric Vander Wal ◽  
Ryan K. Brook ◽  
Erin M. Bayne ◽  
Philip D. McLoughlin

2002 ◽  
Vol 157 (2-3) ◽  
pp. 281-300 ◽  
Author(s):  
Mark S Boyce ◽  
Pierre R Vernier ◽  
Scott E Nielsen ◽  
Fiona K.A Schmiegelow

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