Organic fertilization of peach trees: implication on nitrogen availability, root growth and carbon distribution within plant

In the last years, sustainable horticulture has been increasing; however, to be successful this practice needs an efficient soil fertility management to maintain a high productivity and fruit quality standards. For this purpose composted organic materials from agri-food industry and municipal solid waste has been used as a source to replace chemical fertilizers and increase soil organic matter. To better understand the influence of compost application on soil fertility and plant growth, we carried out a study comparing organic and mineral nitrogen (N) fertilization in micro propagated plants, potted trees and commercial peach orchard with these aims: 1. evaluation of tree development, CO2 fixation and carbon partition to the different organs of two-years-old potted peach trees. 2. Determination of soil N concentration and nitrate-N effect on plant growth and root oxidative stress of micro propagated plant after increasing rates of N applications. 3. Assessment of soil chemical and biological fertility, tree growth and yield and fruit quality in a commercial orchard. The addition of compost at high rate was effective in increasing CO2 fixation, promoting root growth, shoot and fruit biomass. Furthermore, organic fertilizers influenced C partitioning, favoring C accumulation in roots, wood and fruits. The higher CO2 fixation was the result of a larger tree leaf area, rather than an increase in leaf photosynthetic efficiency, showing a stimulation of plant growth by application of compost. High concentrations of compost increased total soil N concentration, but were not effective in increasing nitrate-N soil concentration; in contrast mineral-N applications increased linearly soil nitrate-N, even at the lowest rate tested. Soil nitrate-N concentration influenced positively plant growth at low rate (60- 80 mg kg-1), whereas at high concentrations showed negative effects. In this trial, the decrease of root growth, as a response to excessive nitrate-N soil concentration, was not anticipated by root oxidative stress. Continuous annual applications of compost for 10 years enhanced soil organic matter content and total soil N concentration. Additionally, high rate of compost application (10 t ha-1 year-1) enhanced microbial biomass. On the other hand, different fertilizers management did not modify tree yield, but influenced fruit size and precocity index. The present data support the idea that organic fertilizers can be used successfully as a substitute of mineral fertilizers in fruit tree nutrient management, since they promote an increase of soil chemical and biological fertility, prevent excessive nitrate-N soil concentration, promote plant growth and potentially C sequestration into the soil.

Heuristic algorithms for the Capacitated Location-Routing Problem and the Multi-Depot Vehicle Routing Problem

The Capacitated Location-Routing Problem (CLRP) is a NP-hard problem since it generalizes two well known NP-hard problems: the Capacitated Facility Location Problem (CFLP) and the Capacitated Vehicle Routing Problem (CVRP). The Multi-Depot Vehicle Routing Problem (MDVRP) is known to be a NP-hard since it is a generalization of the well known Vehicle Routing Problem (VRP), arising with one depot. This thesis addresses heuristics algorithms based on the well-know granular search idea introduced by Toth and Vigo (2003) to solve the CLRP and the MDVRP. Extensive computational experiments on benchmark instances for both problems have been performed to determine the effectiveness of the proposed algorithms. This work is organized as follows: Chapter 1 describes a detailed overview and a methodological review of the literature for the the Capacitated Location-Routing Problem (CLRP) and the Multi-Depot Vehicle Routing Problem (MDVRP). Chapter 2 describes a two-phase hybrid heuristic algorithm to solve the CLRP. Chapter 3 shows a computational comparison of heuristic algorithms for the CLRP. Chapter 4 presents a hybrid granular tabu search approach for solving the MDVRP.