The Allocation of Sugarbeet Production Contracts: An Application of Calibrated Production Equilibrium Modelling

Abstract

The potential of this approach to simulate the farm level impacts of these policies is clearly shown here. The preliminary results suggest that reallocation of existing production contracts between producers according to a market-type mechanism, can generate small positive benefits for the farmers in the sample investigated here. Should reductions in contracts be deemed necessary a market-type mechanism can accomplish the necessary reallocation at lower cost to the farmers than a uniform reduction of contracted tonnage across all farmers. It is possible that a self-financing 'outgoer ' type scheme might generate benefits similar to those associated with a market-type mechanism.* UK currently has 8 per cent (white sugar equivalent) of the total EU sugar quota. Ninety-one per cent of this quota is designated A-quota, the remaining nine per cent is referred to as B-quota. Sugar production over and above these amounts, often referred to as C-quota sugar, is not supported under CAP. British Sugar plc, as the sole British processor of sugar from sugarbeet, has been allocated all of the UK quota. t There is also the introduction of a permanent provision for cane refining because of Portugal and the commitments made in 1986 at its time of joining the EU.

A multi-agent simulation model for spatial optimisation of manure allocation

“…Despite the reality of strong rigidities and transaction costs in quota markets, their modelling is often based on a perfect market for quota rights (Alvarez et al, 2006;Brannlund et al, 1998;Bureau et al, 1997;Fraser et al, 1997;Mahler, 1994;Van Passel et al, 2006). ) with E mft being the amount of exported manure of manure type m from the farm to transport firm t, I mft the amount of incoming manure of manure type m at the farm from transport firm t and T mt1t2 the amount of manure of manure type m transported from transport firm t 1 to transport firm t 2 .…”

Positive Mathematical Programming for Farm Planning: Review

“…Such applications can be widely observed in the field of farm management and in related fields such as bio-economic farm modeling 20 and hydro-economic regional modeling 39 . The scope of analysis ranges from the farm level 1,2,8,29,30 to the regional and interregional level 5,7,9,21,24,25,26,33,36 to the national level 11 . In the majority of applications, the quadratic terms of the objective function were attributed, as in the present paper, to the linearly increasing average cost function, and the rest of the studies often attributed the terms to the linearly decreasing average yield function 2,16,30,40 .…”

“…Parameter c is specified on the basis of farm management data (see Nakashima 29 for the procedure), and once specified, it is denoted as ĉ. Given that the optimum dual variables associated with resource constraints coincide both in the QP model (1)- (3) and the LP model (4)- (7) The problem is that, without additional information, equation (8) does not have a unique solution in d and Q because there are fewer equations than unknowns to be calibrated (i.e., n < n + 0.5n (n + 1)), which is why this type of calibration is referred to as an ill-posed problem 32 . To solve the ill-posed problem, the following assumption has frequently been imposed: Assumption 1: Matrix Q is diagonal; that is, q ij = 0 if i ≠ j, where q ij (i = 1,2,…, n; j = 1,2,…, n) are the elements of Q. Assumptions 2.1 and 2.2 reduce the number of equations to match the number of parameters to be calibrated; therefore, respective unique solutions can be derived from equation (9) as…”