1982
DOI: 10.1016/0308-521x(82)90089-0
|View full text |Cite
|
Sign up to set email alerts
|

Linear programming for repeated use in the analysis of agricultural systems

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

1985
1985
2009
2009

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 9 publications
(3 citation statements)
references
References 14 publications
0
3
0
Order By: Relevance
“…[1] Brokken, R. "Programming Models for Use of the Lofgreen-Garrett Net Energy System in Formulating Rations for Beef 5For a general discussion of matrix generators and report writers in applications of this type see McCarl and Nuthall [9]. 6Readers may refer to papers by Brokken [2], Burt 31], and Epplin, Shashanka and Heady [61 for discussions of issues related to estimating mathematical models of beef production.…”
Section: Referencesmentioning
confidence: 99%
See 1 more Smart Citation
“…[1] Brokken, R. "Programming Models for Use of the Lofgreen-Garrett Net Energy System in Formulating Rations for Beef 5For a general discussion of matrix generators and report writers in applications of this type see McCarl and Nuthall [9]. 6Readers may refer to papers by Brokken [2], Burt 31], and Epplin, Shashanka and Heady [61 for discussions of issues related to estimating mathematical models of beef production.…”
Section: Referencesmentioning
confidence: 99%
“…Recent applications of these procedures include sector analysis and risk programming [5,9], (see [7], [8], or (10] for detailed discussions of grid linearization techniques). In discussing the grid linear approximations of the NRC equations, explicit parameter values for mediumframe steers will be used.…”
Section: Incorporating Explicit Nutrient Requirement Equationsmentioning
confidence: 99%
“…Linear programming has been applied to farm household research for several decades. Its advantages are the structured data requirements, which provide a good insight into the studied systems, the flexibility of model structures and the ease with which model runs can be replicated with various data sets (McCarl and Nuthall 1982). Disadvantages are several fundamental assumptions which underlie linear programming models, such as linearity, additivity, divisibility, certainty and non-negativity (Paris 1991).…”
Section: Linear Programming Modelsmentioning
confidence: 99%