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Open AccessArticle

Optimising Crop Selection for Small Urban Food Gardens in Dry Climates

by James Ward 1 and John Symons 2,*
School of Natural and Built Environments, University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA 5095, Australia
Victoria Institute of Strategic Economic Studies, Victoria University, 300 Flinders Street, Melbourne, VIC 3000, Australia
Author to whom correspondence should be addressed.
Academic Editor: Douglas D. Archbold
Horticulturae 2017, 3(2), 33;
Received: 15 July 2016 / Revised: 10 February 2017 / Accepted: 5 May 2017 / Published: 10 May 2017
The net value of urban agriculture has not been studied, especially accounting for the cost of water. This study has sought to remedy this gap in the literature by examining the varying price of water in different climates. A two-stage linear programming model has been used to maximise the net value of urban agriculture. The decision variables included the type and yield of crops; constraints included upper and lower bounds of dietary food groups, individual foods, protein and energy as well as area utilised per person. The results show optimal crop regimes are similar across different climates and water prices due to the selection of crops that have high profit margins. The results also showed that per capita garden size is critical with smaller gardens optimal in terms of water applied per unit area and net value returned as well as return per unit area due to the ability to select the highest value crops. Generally, the more high-value and low water-use crops that can be included, the higher the value in larger gardens. The results indicate that a modest food garden growing the right crops can be highly cost-effective, even with conservative crop yields and water use. View Full-Text
Keywords: urban food garden; linear programming; water; optimisation urban food garden; linear programming; water; optimisation
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Ward, J.; Symons, J. Optimising Crop Selection for Small Urban Food Gardens in Dry Climates. Horticulturae 2017, 3, 33.

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