Effects of Irrigation and Tree Spacing on Soil and Air Temperature Profiles of Olive Orchards

Changes on the climate of the boundary layer occur when a vegetation cover above a bare soil is introduced, namely on temperatures and humidity profiles, above and under soil surface. Since air and soil temperatures affect crop growth and development and also soil moisture, they have been used as driving variables in numerous crop growth and development models as well as in those referred to soil mineralization, evaporation, transpiration, etc. The aim of this work was to evaluate (a) the soil thermal behavior in two olive orchards (Olea europaea sp. europaea), both grown on soils with little profile development (Regosol and Cambisol) and subject to drip irrigation but with different spacing between trees, and (b) the air thermal profile over olive rows. Experiments were performed from April to June 2012 in Southern Portugal. Soil and air temperatures were measured by thermocouples. The two orchards changed spatial distribution of soil surface temperature, soil temperature profiles and air temperature within the canopy, either on a daily or hourly basis. Olive tree spacing and irrigation affected both the soil thermal behavior and air thermal profiles. Tree spacing affected the horizontal gradients established along the interrows (intensity and rhythm). Irrigation reduced hourly and daily mean soil surface temperatures and daily thermal amplitudes of both profiles. Differences were also found on damping depths of the thermal wave estimated for the driest and the wettest profiles. Along the row, the effect of shading seems to overlap that of irrigation in a hourly basis

Local Physical Coodinates from Symplectic Projector Method

The basic arguments underlying the symplectic projector method are presented. By this method, local free coordinates on the constrait surface can be obtained for a broader class of constrained systems. Some interesting examples are analyzed.Comment: 8 page

A linear programming based heuristic framework for min-max regret combinatorial optimization problems with interval costs

This work deals with a class of problems under interval data uncertainty, namely interval robust-hard problems, composed of interval data min-max regret generalizations of classical NP-hard combinatorial problems modeled as 0-1 integer linear programming problems. These problems are more challenging than other interval data min-max regret problems, as solely computing the cost of any feasible solution requires solving an instance of an NP-hard problem. The state-of-the-art exact algorithms in the literature are based on the generation of a possibly exponential number of cuts. As each cut separation involves the resolution of an NP-hard classical optimization problem, the size of the instances that can be solved efficiently is relatively small. To smooth this issue, we present a modeling technique for interval robust-hard problems in the context of a heuristic framework. The heuristic obtains feasible solutions by exploring dual information of a linearly relaxed model associated with the classical optimization problem counterpart. Computational experiments for interval data min-max regret versions of the restricted shortest path problem and the set covering problem show that our heuristic is able to find optimal or near-optimal solutions and also improves the primal bounds obtained by a state-of-the-art exact algorithm and a 2-approximation procedure for interval data min-max regret problems