Establishing limits to agriculture and afforestation: A GIS based multi-objective approach to prevent algal blooms in a coastal lagoon

Lorena Rodríguez-Gallego; Antonella Barletta Carolina Cabrera; Carla Kruk; Mariana Nin; Antonio Mauttone

doi:10.3934/jdg.2019012

Article Contents

2019, Volume 6, Issue 2: 159-178. Doi: 10.3934/jdg.2019012

This issue Previous Article Economic evolution and uncertainty: Transitions and structural changes Next Article

Establishing limits to agriculture and afforestation: A GIS based multi-objective approach to prevent algal blooms in a coastal lagoon

1.
Centro Universitario Regional del Este, Universidad de la República, Ruta 9 y Ruta 15, Rocha, Uruguay
2.
Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, Uruguay
3.
Vida Silvestre Uruguay, Canelones 1198, Montevideo, Uruguay
4.
Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, Montevideo, Uruguay

^* Corresponding author: dunachirca@gmail.com
^* Corresponding author: dunachirca@gmail.com

Received: December 2018

Revised: April 2019

Published: April 2019

This work was supported by CSIC-Udelar, ANII and PEDECIBA.

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Abstract

Biodiversity conservation and ecosystem services provision can compete with other land uses due to its incompatibility with many productive activities. The need for multifunctional landscapes that simultaneously provide food security, maintenance of ecological functions and fulfill welfare requirements is evident. Multi-objective optimization procedures can select between different land uses in each parcel of the territory, simultaneously satisfying contrasting objectives. Each solution (a map) represents a spatial configuration of land uses, generating spatial alternatives and offering flexibility to conduct discussions among social actors. In order to prevent eutrophication we developed a methodological approach for planning land-use transformations in productive territories, considering ecological processes from the beginning. A two-objective approach was used to allocate different land uses in the most suitable sites (objective one) that simultaneously minimize nutrients exportation (objective two). The land uses allocation was Pareto optimal and was conducted by integer linear programming. According to the relative importance given to each objective, two types of land use allocation were obtained, one dominated by agriculture but where a threshold of phosphorus load was exceeded, and another one where conservation and livestock ranching on natural grasslands prevailed and the phosphorus load decreased dramatically. This spatially explicit tool helps decision makers to design multifunctional landscapes for sustainable development and promote social discussions.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. Laguna de Rocha watershed. Land cover in 2011 and the protected area boundary are indicated

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Figure 2. Land suitability maps for a) agriculture, b) afforestation and c) livestock ranching and biodiversity conservation, obtained from [53]. Detailed explanation of each map construction is presented in Methods

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Figure 3. Steps followed for the determination of: a) the phosphorus load exported by the watershed and b) the phosphorus load that the lagoon can receive without promoting the growth of cyanobacteria, over a year

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Figure 4. Maps of optimal allocation of land uses in Laguna de Rocha watershed obtained for different $ \alpha $ values

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Figure 5. Shows the number of pixels assigned to each land cover for the different levels of $ \alpha $. The P threshold for cyanobacteria growth is located between $ \alpha $ values of 0.234 and 0.24

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Figure 6. In black are shown the pixels were the optimization procedure allocated agriculture and also have high conservation and livestock ranching suitability (values = 0.5) in the catchment area of Laguna de Rocha. Left figure: $ \alpha $ = 0.0001 to 0.1 and right figure: $ \alpha $ = 0.1. For higher $ \alpha $ values the model stops allocating agriculture in suitable pixels for G+C. The number of pixels selected (in black) in both cases are 13,038 (6,519 ha) and 893 (446.5 ha), respectively

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Figure 7. In black are shown the pixels were the optimization procedure allocated conservation and livestock ranching and also have high agriculture suitability (values = 0.5) in the catchment area of Laguna de Rocha Left figure: $ \alpha $ = 0.235 and right figure: $ \alpha $ = 0.24. The number of pixels selected (in black) in both cases are 13,276 (6,638 ha) and 22,079 (22,079 ha), respectively

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Table 1. Description of land covers of Laguna de Rocha watershed from a 2011 Landsat image (obtained from [42]). The table indicates the land use that is currently carried out in each cover, and the land use that each land cover can take after optimization. A: agriculture, F: afforestation, G + C: livestock ranching and conservation. The pixels with land covers that cannot change during modeling were not included for the optimization procedure

Land cover	Description	Land use	Land use that pixels can take after modeling
Grasslands	Soils covered by natural grasslands (Fig. 1)	Livestock ranching & conservation (G+C)	A, F, G+C
Flooded grasslands and wetlands	Areas near by water bodies that are flooded frequently (Fig. 1)	G+C	G+C
Dunes	Dunes and sand deposits, mostly in the coastal zone (Fig. 1)	G+C	G+C
Natural forests	Native trees and shrub vegetation (Fig. 1)	G+C	G+C
Agriculture	Agriculture in 2011 image (taken from [42]), it includes mainly cereals crops and artificial prairies, and also oversowing, tiled lands, stovers, forage crops and horticulture (Fig. 1).	Mainly agriculture and artificial prairies (A)	A, F, G+C
Afforestation	Afforested lands in 2011 image with exotic species to supply industry (Fig. 1)	Afforestation (F)	F
Buffer strip	Lands adjacent to the streams, the width is variable and proportional to the order of the streams (Fig. 1)	G+C or A or F	G+C

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Table 2. Phosphorus export from each land cover. It shows the number of pixels occupied by each land cover in 2011, the export coefficients and the TP load that each cover exports. Each pixel has a surface area of half a hectare

Land cover	Number of Pixels	P Export Coefficient (kgP/ha/y)	P load exported by each land cover (kg P/y)
Dunes and sand deposits	495	0.01	2.5
Natural forests	17545	0.01 [22]	87.7
Buffer strips	74799	0.01 (the same as wetlands)	374.0
Afforestation	7611	0.29 [5]	1104.0
Wetlands and flooded grasslands	26082	0.01 [31]	130.4
Grasslands	83073	0.24 (Adapted from [19])	9968.8
Agriculture	12866	2.6 (average between values for cereals [35] and artificial praires [51])	16725.8
TOTAL	22471		28392.8

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Table 3. Surface area of the different land uses of the optimization trials shown in Fig. 4. The value of $ \alpha $ and the load of TP is indicated

α	Agriculture (ha)	Livestock ranching and conservation (ha)	Afforestation (ha)	Surface area not included in the model (ha)	P load (kg - year)
0.01	25,487.5	13,100	9,382	63,266	73,830.5
0.1	17,707	20,880.5	9,382	63,266	55,468.5
0.235	16,792	21,795.5	9,382	63,266	53,309.1
0.240	1,351	37,236.5	9,382	63,266	16,868.3

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