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An important problem in rural-area supply chains is how to transport the harvested fruit to urban areas. Low-and medium-capacity vehicles are used in Colombia to carry out this activity. Operating them comes with an inherent cost and generates carbon emissions. Normally, minimizing operating costs and minimizing carbon emissions are conflicting objectives to allocate such vehicles efficiently in any of the supply chain echelons. We designed a multi-objective mixed-integer programming model to address this problem and solved it via the ε-constraint method. It includes decisions mainly about quantities of fruit to transport and store, types of vehicles to allocate according to their capacities, CO 2 emission levels of these vehicles, and subcontracting on the collection process. The main results show two schedules for allocating the vehicles, showing minimum and maximum CO 2 emissions. Minimum CO 2 emissions scheme require subcontracting and the maximum CO 2 scheme does not. Then, a Pareto frontier shows that CO 2 emissions level are inversely proportional to total management cost for different scenarios in which fruit supply was modified.

Food security is among the most pressing global concerns. It is principally threatened by the combination of rural migration and the pressure of climate change. In order to mitigate these effects, the need to promote stable conditions for small producers -who generate 80% of the world's food- has arose. In search to improve market conditions, this study aims to evaluate the feasibility of cross-hedging between electrical derivatives market and spot agricultural products in Colombia. This hypothesis is proposed, as Colombia depends upon hydro-electricity, an electricity source which is heavily influenced by climatic conditions, particularly the “El Niño” southern oscillation (ENSO). The prices of agricultural products are thus volatile, and subject to this phenomenon. ENSO is presumed to be an important link between these two markets. To contrast the hypothesis, the most commonly- methods in cross-hedging literature were employed to estimate hedge ratios: OLS, Error Correction Models, and GARCH estimations. This last estimation was found to be the one with the best performance for hedge ratio estimation. Despite this, of 93 products analyzed, statistically significant relationships were found for only nine. Besides, it was found that cross-hedging contributes to a risk reduction of not more than 32%.

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