Nutritive characteristics of irrigated perennial pastures in northern Victoria and the selection of nutrients by grazing dairy cows

2001 ◽  
Vol 41 (5) ◽  
pp. 601 ◽  
Author(s):  
C. R. Stockdale ◽  
D. C. Cohen ◽  
P. T. Doyle

The aims of the work reported here were, first, to summarise the available information on nutritive characteristics of perennial pastures in northern Victoria and to examine factors that affected these characteristics. Second, in order for farmers to use such information, we needed to confirm that estimates of nutritive characteristics and selection differentials (the ratio of the concentration of a nutrient in herbage consumed relative to that in herbage on offer) defined in research with irrigated pastures in northern Victoria could be applied to commercial dairy farm situations. Finally, regression models were used to relate selection differentials for in vitro dry matter digestibility (DMD), crude protein (CP), neutral detergent fibre (NDF) and acid detergent fibre (ADF) to pasture conditions at different times of the year. It was hypothesised that pre- and post-grazing pasture height (or mass), botanical composition and season are the factors most likely to affect the nutritive characteristics of irrigated perennial pastures and the prediction of selection differentials. Research and on-farm data were generally consistent. On average, DMD of the pasture on offer was highest in August at 80%, declining to 65% in March before starting to rise again. The trends in NDF tended to be the inverse of those for DMD although NDF peaked in late summer (at 63.6%), rather than early autumn. Crude protein was highest in July (21.3%), declining to 13.4% at the start of autumn. Except for winter, increases in herbage mass generally had a significant (P<0.05) negative effect on DMD and CP, and a significant (P&lt;0.05) positive influence on NDF. Of the species variables, weeds and paspalum influenced all nutritive characteristics in summer and autumn, while the proportion of dead material was generally important at most times of the year. Increases in weeds, paspalum and dead material significantly (P<0.05) decreased DMD and CP, and significantly (P<0.05) increased NDF. Selection differentials for summer were closer to unity for DMD and NDF than at other times of the year (P<0.05). Selection differentials for CP in summer were the same (P<0.05) as the mean values recorded in all other seasons. Significant (P<0.05) factors that accounted for variation in the selection differential data included botanical composition and seasonal variables, with the clover and dead components being the most consistent. In addition, post-grazing height and/or the amount of herbage removed at grazing significantly (P<0.05) affected selection differentials of all the nutritive characteristics. For DMD and CP, the greater the amount of herbage removed, the lower the selection differential; the reverse was true for NDF and ADF. It is concluded that regressions generated from the data collated here could be used in decision support tools to help inform farmers about the nutrients their cows obtain from the pasture they graze. This means that, when feeding supplements, farmers can make better decisions about the provision of the most appropriate feeds to meet cow requirements. In the absence of decision support tools that have incorporated the information generated in this study, we suggest that the same average selection differentials could be applied in all dairying areas of Victoria to perennial ryegrass–white clover-based pastures during spring. These are 1.1, 1.3 and 0.8 for DMD, CP and NDF, respectively. These values might also be used in autumn, but with less certainty because of differences in the timing of the autumn break and greater variations in species composition.

2020 ◽  
pp. 323
Author(s):  
Nour Elislam Djedaa ◽  
Abderrezak Moulay Lakhdar

2007 ◽  
Vol 7 (5-6) ◽  
pp. 53-60
Author(s):  
D. Inman ◽  
D. Simidchiev ◽  
P. Jeffrey

This paper examines the use of influence diagrams (IDs) in water demand management (WDM) strategy planning with the specific objective of exploring how IDs can be used in developing computer-based decision support tools (DSTs) to complement and support existing WDM decision processes. We report the results of an expert consultation carried out in collaboration with water industry specialists in Sofia, Bulgaria. The elicited information is presented as influence diagrams and the discussion looks at their usefulness in WDM strategy design and the specification of suitable modelling techniques. The paper concludes that IDs themselves are useful in developing model structures for use in evidence-based reasoning models such as Bayesian Networks, and this is in keeping with the objectives set out in the introduction of integrating DSTs into existing decision processes. The paper will be of interest to modellers, decision-makers and scientists involved in designing tools to support resource conservation strategy implementation.


2021 ◽  
Vol 167 ◽  
pp. 112313
Author(s):  
Zhaoyang Yang ◽  
Zhi Chen ◽  
Kenneth Lee ◽  
Edward Owens ◽  
Michel C. Boufadel ◽  
...  

2021 ◽  
Vol 13 (10) ◽  
pp. 5744
Author(s):  
Innocent K. Tumwebaze ◽  
Joan B. Rose ◽  
Nynke Hofstra ◽  
Matthew E. Verbyla ◽  
Daniel A. Okaali ◽  
...  

User-friendly, evidence-based scientific tools to support sanitation decisions are still limited in the water, sanitation and hygiene (WASH) sector. This commentary provides lessons learned from the development of two sanitation decision support tools developed in collaboration with stakeholders in Uganda. We engaged with stakeholders in a variety of ways to effectively obtain their input in the development of the decision support tools. Key lessons learned included: tailoring tools to stakeholder decision-making needs; simplifying the tools as much as possible for ease of application and use; creating an enabling environment that allows active stakeholder participation; having a dedicated and responsive team to plan and execute stakeholder engagement activities; involving stakeholders early in the process; having funding sources that are flexible and long-term; and including resources for the acquisition of local data. This reflection provides benchmarks for future research and the development of tools that utilize scientific data and emphasizes the importance of engaging with stakeholders in the development process.


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