April  2019, 6(2): 159-178. doi: 10.3934/jdg.2019012

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

Received  December 2018 Revised  April 2019 Published  April 2019

Fund Project: This work was supported by CSIC-Udelar, ANII and PEDECIBA

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.

Citation: Lorena Rodríguez-Gallego, Antonella Barletta Carolina Cabrera, Carla Kruk, Mariana Nin, Antonio Mauttone. Establishing limits to agriculture and afforestation: A GIS based multi-objective approach to prevent algal blooms in a coastal lagoon. Journal of Dynamics & Games, 2019, 6 (2) : 159-178. doi: 10.3934/jdg.2019012
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[12]

S. Carpenter, Eutrophication of aquatic ecosystems: Bistability and soil phosphorus, Proceedings of the National Academy of Sciences, 102 (2005), 10002-10005. doi: 10.1073/pnas.0503959102. Google Scholar

[13]

P. ChopingT. DoréL. Guindé and J.-M. Blazy, MOSAICA: A multi-scale bioeconomic model for the design and ex ante assessment of cropping system mosaics, Agricultural Systems, 140 (2015), 26-39. Google Scholar

[14]

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D. Conde, L. Rodríguez-Gallego, D. de Álava, N. Verrastro, C. Chreties, X. Lagos, S. Solari, G. Piñeiro, L. Teixeira, L. Seijo, J. Vitancurt, H. Caymaris and D. Panario, Solutions for sustainable coastal lagoon management: From conflict to the implementation of a consensual decision tree for artificial opening, in Coastal Zones: Solutions for the 21st Century (eds. Baztan et al.), Elsevier, Amsterdam, (2015), 217–250.Google Scholar

[16]

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[17]

Deltares, D-Water Quality, Water quality and aquatic ecology modelling suite. User Manual - Water Quality and Aquatic Ecology, and Technical Reference Manual - Processes Library Description., Delft, The Netherlands, 2013.Google Scholar

[18]

W. Dodds and R. Oakes, Controls on nutrients across a prairie stream watershed: Land use and riparian cover effects, Environmental Management, 37 (2006), 634-646. doi: 10.1007/s00267-004-0072-3. Google Scholar

[19]

J. DrewryL. NewhamR. GreeneA. Jakeman and B. Croke, A review of nitrogen and phosphorus export to waterways: Context for catchment modeling, Marine and Freshwater Research, 58 (2006), 757-774. doi: 10.1071/MF05166. Google Scholar

[20]

M. Ehrgott and X. Gandibleux, Multiobjective combinatorial optimization, in Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys. International Series in Operations Research & Management Science (eds. M. Ehrgott and X. Gandibleux), Springer, Berlin, 52 (2002), 369–444. doi: 10.1007/0-306-48107-3_8. Google Scholar

[21]

M. Ehrgott, Multicriteria Optimization, Springer, Berlin, 2005. Google Scholar

[22]

EPA, Methods for Evaluating Wetland Condition: Land-Use Characterization for Nutrient and Sediment Risk Assessment, Office of Water, U.S. Environmental Protection Agency, Washington, DC. EPA-822-R-02-025, 2002.Google Scholar

[23]

K. FarleyE. Jobbágy and R. Jackson, Effects of afforestation on water yield: A global synthesis with implications for policy, Global Change Biology, 11 (2005), 1565-1576. doi: 10.1111/j.1365-2486.2005.01011.x. Google Scholar

[24]

T. Fischer and V. Onyago, Strategic environmental assessment-related research projects and journal articles: an overview of the past 20 years, Impact Assessment and Project Appraisal, 30 (2012), 253-263. doi: 10.1080/14615517.2012.740953. Google Scholar

[25]

R. FourerD. Gay and B. Kernighan, A Modeling Language for Mathematical Programming, Management Science, 36 (1990), 519-554. doi: 10.1287/mnsc.36.5.519. Google Scholar

[26]

F. García Préchac, O. Ernst, P. Arbeleche, M. Pérez Bidegain, C. Pritsch, A. Ferenczi and M. Rivas, Intensificación Agrícola: Oportunidades y Amenazas Para un País Productivo y Natural, Fondo Universitario para Contribuir a la Comprensión Pública de Temas de Interés General. UdelaR-CSIC Colección Art. 2. Montevideo, 2010.Google Scholar

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H. GodfrayI. BeddingtonL. HaddadD. LawrenceJ. MuirJ. PrettyS. RobinsonS. Thomas and C. Toulmin, Food security: The challenge of feeding 9 billion people, Science, 327 (2010), 812-818. doi: 10.1126/science.1185383. Google Scholar

[28]

D. Gordon, P. Boudreau, H. Mann, J. Ong, W. Sivert, S. Smith, G. Wattayakorn, P. Wulff and T. Yanagi, LOICZ Biogeochemical modeling guidelines, in LOICZ Reports and Studies: N. 5, 1996.Google Scholar

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J. Groot and W. Rossing, Model-aided learning for adaptive management of natural resources: an evolutionary design perspective, Methods in Ecology and Evolution, 2 (2011), 643-650. doi: 10.1111/j.2041-210X.2011.00114.x. Google Scholar

[30]

H. HuB. Fu and Z. Zheng, SAORES: a spatially explicit assessment and optimization tool for regional ecosystem services, Landscape Ecology, 30 (2015), 547-560. doi: 10.1007/s10980-014-0126-8. Google Scholar

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Figure 1.  Laguna de Rocha watershed. Land cover in 2011 and the protected area boundary are indicated
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
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
Figure 4.  Maps of optimal allocation of land uses in Laguna de Rocha watershed obtained for different $ \alpha $ values
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
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
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
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 coverDescriptionLand useLand use that pixels can take after modeling
GrasslandsSoils covered by natural grasslands (Fig. 1)Livestock ranching & conservation (G+C)A, F, G+C
Flooded grasslands and wetlandsAreas near by water bodies that are flooded frequently (Fig. 1)G+CG+C
DunesDunes and sand deposits, mostly in the coastal zone (Fig. 1)G+CG+C
Natural forestsNative trees and shrub vegetation (Fig. 1)G+CG+C
AgricultureAgriculture 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
AfforestationAfforested lands in 2011 image with exotic species to supply industry (Fig. 1)Afforestation (F)F
Buffer stripLands adjacent to the streams, the width is variable and proportional to the order of the streams (Fig. 1)G+C or A or FG+C
Land coverDescriptionLand useLand use that pixels can take after modeling
GrasslandsSoils covered by natural grasslands (Fig. 1)Livestock ranching & conservation (G+C)A, F, G+C
Flooded grasslands and wetlandsAreas near by water bodies that are flooded frequently (Fig. 1)G+CG+C
DunesDunes and sand deposits, mostly in the coastal zone (Fig. 1)G+CG+C
Natural forestsNative trees and shrub vegetation (Fig. 1)G+CG+C
AgricultureAgriculture 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
AfforestationAfforested lands in 2011 image with exotic species to supply industry (Fig. 1)Afforestation (F)F
Buffer stripLands adjacent to the streams, the width is variable and proportional to the order of the streams (Fig. 1)G+C or A or FG+C
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 coverNumber of PixelsP Export Coefficient (kgP/ha/y) P load exported by each land cover (kg P/y)
Dunes and sand deposits4950.012.5
Natural forests175450.01 [22]87.7
Buffer strips747990.01 (the same as wetlands)374.0
Afforestation76110.29 [5]1104.0
Wetlands and flooded grasslands260820.01 [31]130.4
Grasslands830730.24 (Adapted from [19])9968.8
Agriculture128662.6 (average between values for cereals [35] and artificial praires [51])16725.8
TOTAL22471 28392.8
Land coverNumber of PixelsP Export Coefficient (kgP/ha/y) P load exported by each land cover (kg P/y)
Dunes and sand deposits4950.012.5
Natural forests175450.01 [22]87.7
Buffer strips747990.01 (the same as wetlands)374.0
Afforestation76110.29 [5]1104.0
Wetlands and flooded grasslands260820.01 [31]130.4
Grasslands830730.24 (Adapted from [19])9968.8
Agriculture128662.6 (average between values for cereals [35] and artificial praires [51])16725.8
TOTAL22471 28392.8
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)
0.0125,487.513,1009,38263,26673,830.5
0.117,70720,880.59,38263,26655,468.5
0.23516,79221,795.59,38263,26653,309.1
0.2401,35137,236.59,38263,26616,868.3
α Agriculture (ha) Livestock ranching and conservation (ha) Afforestation (ha) Surface area not included in the model (ha) P load (kg - year)
0.0125,487.513,1009,38263,26673,830.5
0.117,70720,880.59,38263,26655,468.5
0.23516,79221,795.59,38263,26653,309.1
0.2401,35137,236.59,38263,26616,868.3
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