The role of nutrients for improving seedling quality in drylands

Forest plantations have been extensively used to combat desertification. In drylands, harsh climate conditions and unfertile soils often preclude seedling establishment. The improvement in seedling quality by manipulating nutrient availability could contribute to increase planting success. However, morpho-functional traits defining optimum seedling quality in drylands, and the fertilization schemes to achieve them, are still under discussion. Several studies suggest that well fertilized seedlings may perform better than nutrient limited seedlings in these environments. However, recent works have shown opposite results. In this review, we discuss the concept of seedling quality in drylands based on an evaluation of the effects of nutrient manipulation on seedling morpho-functional traits and field performance. According to existing data, we hypothesize that nutrient-limited small seedlings may be better adapted to arid environments and unfavorable microsites, where access to water is uncertain and a conservative water use strategy may be advantageous. In contrast, in dry sub-humid areas, areas with deep soils, protected from excess radiation, and areas where irrigation is feasible, well-fertilized big seedlings with high root growth potential may have more chances of success. We discuss this theory in the context of the multiple objectives of dryland restoration and the environmental constrains posed by these areas, and identify knowledge gaps that should be targeted to test our hypothesis.

Mathematical programming model for heat exchanger design through optimization of partial objectives

Mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell–Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature.