scholarly journals Ranking Dairy Sires by a Linear Programming Dairy Farm Model

1984 ◽  
Vol 67 (12) ◽  
pp. 3015-3024 ◽  
Author(s):  
S. Sivarajasingam ◽  
E.B. Burnside ◽  
J.W. Wilton ◽  
W.C. Pfeiffer ◽  
D.G. Grieve
2016 ◽  
Vol 45 (4) ◽  
pp. 181-189 ◽  
Author(s):  
Augusto Hauber Gameiro ◽  
Cleber Damião Rocco ◽  
José Vicente Caixeta Filho

2005 ◽  
Vol 2005 ◽  
pp. 116-116
Author(s):  
P. Chang ◽  
P. Rowlinson ◽  
P. Cain

The Taiwanese government assists their dairy industry by supporting domestic prices through a combination of import restrictions, price support, government purchasing and subsidised disposal of surpluses. As a result, dairy production in Taiwan is insulated from international price trends. However, these dairy assistance policies can not protect the domestic price after Taiwan joins the World Trade Organisation (WTO) at the end of 2001. To access the impact of an open market, linear programming (LP) is applied to model the influence of changing feed price and milk value on dairy farm profitability.


Author(s):  
N. Kaewprathum ◽  
K. Taweeyanyongkul ◽  
S. Pangchuntherk ◽  
S. Choengthong ◽  
K. Boonyanuwat ◽  
...  

Agriculture ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 137 ◽  
Author(s):  
Stefano Gaudino ◽  
Pytrik Reidsma ◽  
Argyris Kanellopoulos ◽  
Dario Sacco ◽  
Martin van Ittersum

Specialised dairy farms are challenged to be competitive and yet respect environmental constrains. A tighter integration of cropping and livestock systems, both in terms of feed and manure flows, can be beneficial for the farm economy and the environment. The greening of the direct payments, which was introduced in the European Union’s greening reform in 2013, is assumed to stimulate the transition towards more sustainable systems. The aim of this study was to quantitatively assess the impacts of greening policies on important economic and environmental indicators of sustainability, and explore potential further improvements in policies. The Farm System SIMulator (FSSIM) bioeconomic farm model was used to simulate the consequences of scenarios of policy change on three representative dairy farms in Piedmont, Italy, i.e., an ‘intensive’, an ‘extensive’, and an ‘organic’ dairy farm. Results showed that in general, there is a large potential to increase the current economic performance of all of the farms. The most profitable activity is milk production, resulting in the allocation of all of the available farm land to feed production. Imposing feed self-sufficiency targets results in a larger adaptation of current managerial practice than the adaptations that are required due to the greening policy scenario. It was shown that the cropping system is not always able to sustain the actual herd composition when 90% feed self-sufficiency is imposed. Regarding the greening policies, it is shown that extensive and organic farms already largely comply with the greening constrains, and the extra subsidy is therefore a bonus, while the intensive farm is likely to sacrifice the subsidy, as adapting the farm plan will substantially reduce profit. The introduction of nitrogen (N)-fixing crops in ecological focus areas was the easiest greening strategy to adopt, and led to an increase in the protein feed self-sufficiency. In conclusion, it is important to note that the greening policy in its current form does not lead to reduced environmental impacts. This implies that in order to improve environmental performance, regulations are needed rather than voluntary economic incentives.


Soil Research ◽  
2012 ◽  
Vol 50 (3) ◽  
pp. 188 ◽  
Author(s):  
Iris Vogeler ◽  
Pierre Beukes ◽  
Alvaro Romera ◽  
Rogerio Cichota

Nitrous oxide (N2O) emissions from agriculture are generally estimated using default IPCC emission factors (EFs) despite the large variation in measured EFs. We used a classification and regression tree (CART) analysis to segregate measured EFs from direct emissions from urine patches and fertiliser and effluent applications, based on temporal and site-specific factors. These segregated EFs were linked to simulations from the DairyNZ Whole Farm Model to obtain N2O emissions for a typical pasture-based dairy farm in New Zealand. The N2O emissions from urine patches, dung pads, and fertiliser and effluent application, as well as from indirect sources, were aggregated to obtain total N2O emissions for the farm-scale. The results, based on segregated EFs, were compared with those obtained using New Zealand-specific EFs. On-farm N2O emissions based on these segregated EFs were 5% lower than those based on New Zealand-specific EFs. Improved farm management by avoiding grazing, effluent, and N fertiliser application during periods of high risk for N2O emissions, or by the use of mitigation technologies such as nitrification inhibitors, could reduce annual farm scale N2O emissions.


Author(s):  
W.E. Prewer ◽  
J.A. Lile ◽  
K.A. Macdonald ◽  
K.P. Bright ◽  
C.C. Palliser ◽  
...  

The frequency of measurements to generate pasture growth rate data varies. Commonly measurements are made once weekly or fortnightly and averaged monthly, using a variety of methods. To assess the effect of frequency of measurement on estimates of pasture growth rate, weekly visual assessment of farm pasture cover was compared with the fortnightly and monthly average and also with predictions by a Whole Farm Model. Weekly observed pasture growth rates had large fluctuations but these were removed when the weekly values were averaged monthly. The fluctuations are due to the variety of paddocks assessed, climatic factors, inconsistencies of operators and inherent errors in the technique used. Values calculated by a Whole Farm Model also showed daily variation in pasture growth rate but the fluctuations were not as severe as those in the observed pasture growth rate data because human error of assessment and error in the technique were removed. Observed monthly pasture growth rates were also compared with values calculated by the model. The model closely predicted observed pasture growth rates for most months. To obtain an accurate estimate for monthly growth rates it is better to average a number of assessments. In the field, this could be an average of weekly observations. Because the model calculates rates daily, it can be used to predict pasture growth rates on a more frequent basis (e.g. weekly) to aid feed budgeting. Keywords: dairy farm, herbage mass, model, pasture growth, simulation


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