Spatial Analysis of Environmentally Sensitive Areas to Soil Degradation Using MEDALUS Model and GIS in Amazonas (Peru): An Alternative for Ecological Restoration
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Used and Processing
2.3. Methodology
2.3.1. Soil Quality Index
2.3.2. Climate Quality Index
Aridity Index
Soil Erosion
2.3.3. Vegetation Cover Index
2.3.4. Management Quality Index
2.3.5. Environmentally Sensitive Areas (ESA)
3. Results
3.1. Spatial Assessment of Constituent Parameters of Quality Indicators
3.2. Spatial Assessment of Quality Indicators
3.3. Environmentally Sensitive Area Index (ESAI)
3.4. Validation of the Index of Environmentally Sensitive Areas
3.5. Correlation Coefficients between Quality Indices
3.6. Ecological Restoration Proposal
4. Discussion
4.1. On Quality Indicators
4.2. On the Environmentally Sensitive Areas Index
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Nortcliff, S.; Hulpke, H.; Bannick, C.G.; Terytze, K.; Knoop, G.; Bredemeier, M.; Schulte-Bisping, H. Soil, 1. Definition, Function, and Utilization of Soil. Ullmann’s Encycl. Ind. Chem. 2011, 33, 399–419. [Google Scholar] [CrossRef]
- Alam, A. Soil Degradation: A Challenge to Sustainable Agriculture. Int. J. Sci. Res. Agric. Sci. 2014, 50–55. [Google Scholar] [CrossRef]
- Karlen, D.L.; Rice, C.W. Soil degradation: Will humankind ever learn? Sustainability 2015, 7, 12490–12501. [Google Scholar] [CrossRef] [Green Version]
- Karamesouti, M.; Detsis, V.; Kounalaki, A.; Vasiliou, P.; Salvati, L.; Kosmas, C. Land-use and land degradation processes affecting soil resources: Evidence from a traditional Mediterranean cropland (Greece). Catena 2015, 132, 45–55. [Google Scholar] [CrossRef]
- Bateman, A.M.; Muñoz-Rojas, M. To whom the burden of soil degradation and management concerns. In Advances in Chemical Pollution, Environmental Management and Protection; Elsevier: Amsterdam, The Netherlands, 2019; Volume 4, pp. 1–22. [Google Scholar] [CrossRef]
- Stavi, I.; Lal, R. Achieving Zero Net Land Degradation: Challenges and opportunities. J. Arid Environ. 2015, 112, 44–51. [Google Scholar] [CrossRef]
- Pacheco, F.A.L.; Sanches Fernandes, L.F.; Valle Junior, R.F.; Valera, C.A.; Pissarra, T.C.T. Land degradation: Multiple environmental consequences and routes to neutrality. Curr. Opin. Environ. Sci. Health 2018, 5, 79–86. [Google Scholar] [CrossRef]
- Lal, R. Restoring soil quality to mitigate soil degradation. Sustainability 2015, 7, 5875–5895. [Google Scholar] [CrossRef] [Green Version]
- Lamqadem, A.A.; Pradhan, B.; Saber, H.; Rahimi, A. Desertification sensitivity analysis using medalus model and gis: A case study of the oases of middle draa valley, morocco. Sensors 2018, 18, 2230. [Google Scholar] [CrossRef] [Green Version]
- Bakhtiari, M.; Darvishi Boloorani, A.; Abdollahi Kakroodi, A.; Rangzan, K.; Mousivand, A. Land degradation modeling of dust storm sources using MODIS and meteorological time series data. J. Arid Environ. 2021, 190, 104507. [Google Scholar] [CrossRef]
- Li, H.; Yang, X.; Zhang, K. Understanding global land degradation processes interacted with complex biophysics and socioeconomics from the perspective of the Normalized Difference Vegetation Index (1982–2015). Glob. Planet. Chang. 2021, 198, 103431. [Google Scholar] [CrossRef]
- Prokop, P. Remote sensing of severely degraded land: Detection of long-term land-use changes using high-resolution satellite images on the Meghalaya Plateau, northeast India. Remote Sens. Appl. Soc. Environ. 2020, 20, 100432. [Google Scholar] [CrossRef]
- Romshoo, S.A.; Amin, M.; Sastry, K.L.N.; Parmar, M. Integration of social, economic and environmental factors in GIS for land degradation vulnerability assessment in the Pir Panjal Himalaya, Kashmir, India. Appl. Geogr. 2020, 125, 102307. [Google Scholar] [CrossRef]
- Prăvălie, R.; Patriche, C.; Tişcovschi, A.; Dumitraşcu, M.; Săvulescu, I.; Sîrodoev, I.; Bandoc, G. Recent spatio-temporal changes of land sensitivity to degradation in Romania due to climate change and human activities: An approach based on multiple environmental quality indicators. Ecol. Indic. 2020, 118, 106755. [Google Scholar] [CrossRef]
- Wijitkosum, S. Reducing vulnerability to desertification by using the spatial measures in a degraded area in Thailand. Land 2020, 9, 49. [Google Scholar] [CrossRef] [Green Version]
- Kosmas, C.; Kirkby, M.J.; Geeson, N. (Eds.) Medalus Project: Mediterranean Desertification and Land Use. Manual on Key Indicators of Desertification and Mapping Environmentally Sensitive Areas. España. 1999. Available online: http://www.comap.ca/kmland/display.php?ID=253&DISPOP=VRCPR (accessed on 20 May 2021).
- Basso, F.; Bove, E.; Dumontet, S.; Ferrara, A.; Pisante, M.; Quaranta, G.; Taberner, M. Evaluating environmental sensitivity at the basin scale through the use of geographic information systems and remotely sensed data: An example covering the Agri basin (Southern Italy). Catena 2000, 40, 19–35. [Google Scholar] [CrossRef]
- Lavado, J.; Schnabel, S.; Goméz, A.; Pulido, M. Mapping sensitivity to land degradation in Extremadura. SW Spain. Land Degrad. Dev. 2009, 607, 591–607. [Google Scholar] [CrossRef]
- Salvati, L.; Bajocco, S. Land sensitivity to desertification across Italy: Past, present, and future. Appl. Geogr. 2011, 31, 223–231. [Google Scholar] [CrossRef]
- De Pina, J.; Baptista, I.; Ferreira, A.J.D.; Amiotte-Suchet, P.; Coelho, C.; Gomes, S.; Amoros, R.; Dos Reis, E.A.; Mendes, A.F.; Costa, L.; et al. Assessment and mapping the sensitive areas to desertification in an insular Sahelian mountain region Case study of the Ribeira Seca Watershed, Santiago Island, Cabo Verde. Catena 2015, 128, 214–223. [Google Scholar] [CrossRef]
- Vieira, R.M.S.P.; Tomasella, J.; Alvalá, R.C.S.; Sestini, M.F.; Affonso, A.G.; Rodriguez, D.A.; Barbosa, A.A.; Cunha, A.P.M.A.; Valles, G.F.; Crepani, E.; et al. Identifying areas susceptible to desertification in the Brazilian northeast. Solid Earth 2015, 6, 347–360. [Google Scholar] [CrossRef]
- Prăvălie, R.; Săvulescu, I.; Patriche, C.; Dumitraşcu, M.; Bandoc, G. Spatial assessment of land degradation sensitive areas in southwestern Romania using modified MEDALUS method. Catena 2017, 153, 114–130. [Google Scholar] [CrossRef]
- Momirović, N.; Kadović, R.; Perović, V.; Marjanović, M.; Baumgertel, A. Spatial assessment of the areas sensitive to degradation in the rural area of the municipality Čukarica. Int. Soil Water Conserv. Res. 2019, 7, 71–80. [Google Scholar] [CrossRef]
- Sommer, S.; Zucca, C.; Grainger, A.; Cherlet, M.; Zougmore, R.; Sokona, Y.; Hill, J.; Della Peruta, R.; Roehrig, J.; Wang, G. Application of indicator systems for monitoring and assessment of desertification from national to global scales. Land Degrad. Dev. 2011, 22, 184–197. [Google Scholar] [CrossRef]
- Shokr, M.S.; Abdellatif, M.A.; El Baroudy, A.A.; Elnashar, A.; Ali, E.F.; Belal, A.A.; Attia, W.; Ahmed, M.; Aldosari, A.A.; Szantoi, Z.; et al. Development of a spatial model for soil quality assessment under arid and semi-arid conditions. sustainability 2021, 13, 2893. [Google Scholar] [CrossRef]
- MINAGRI. Politica Nacional Agraria, Lima, Perú. Available online: https://cdn.www.gob.pe/uploads/document/file/2071814/DECRETO%20SUPREMO%2017-2021-MIDAGRI.pdf (accessed on 10 May 2021).
- Barrientos Felipa, P. La agricultura peruana y su capacidad de competir en el mercado internacional. Equidad Desarro. 2018, 1, 143–179. [Google Scholar] [CrossRef] [Green Version]
- Bedoya Garland, E.; Aramburú, C.E.; Burneo, Z. Una agricultura insostenible y la crisis del barbecho: El caso de los agricultores del valle de los ríos Apurímac y Ene, VRAE. Anthropologica 2017, 35, 211–240. [Google Scholar] [CrossRef] [Green Version]
- Oliva, M.; Quintana, J.L.M.; Guzmán, C.T.; Escalante, W.B. Propiedades fisicoquímicas del suelo en diferentes estadios de la agricultura migratoria en el Área de Conservación Privada “Palmeras de Ocol”, distrito de Molinopampa, provincia de Chachapoyas (departamento de Amazonas). Rev. Investig. Agroprod. Sustent. 2017, 1, 9–21. [Google Scholar] [CrossRef]
- MINAM. Mapa de Áreas Degradadas para la Conservación. Lima, Perú. Available online: https://geoservidor.minam.gob.pe/monitoreo-y-evaluacion/restauracion-de-areas-degradadas/ (accessed on 10 May 2021).
- Salas, R.; Barboza, E.; Oliva, S.M. Dinámica multitemporal de índices de deforestación en el distrito de Florida, departamento de Amazonas, Perú. Rev. Indes. 2016, 2, 1–9. [Google Scholar] [CrossRef]
- Rojas, N.B.; Barboza, E.; Maicelo, J.L.; Oliva, S.M.; Salas, R. Deforestación en la Amazonía peruana: Índices de cambios de cobertura y uso del suelo basado en SIG. Bol. Asoc. Geógr. Espa. 2019, 81, 2538. [Google Scholar]
- Shanee, N.; Shanee, S. Land Trafficking, Migration, and Conservation in the “No-Man’s Land” of Northeastern Peru. Trop. Conserv. Sci. 2016, 9, 1940082916682957. [Google Scholar] [CrossRef] [Green Version]
- Mendoza, M.E.; Salas, R.; Barboza, E. Análisis multitemporal de la deforestación usando la clasificación basada en objetos, distrito de Leymebamba (Perú). Rev. Indes. 2017, 3, 67–76. [Google Scholar] [CrossRef]
- Oliva, C.M.; Collazos, S.R.; Goñas, M.M.; Bacalla, E.; Vigo, M.C.; Vásquez, P.H.; Espinosa, L.S.T.; Maicelo, Q.J.L. Efecto de los sistemas de producción sobre las características físico-químicas de los suelos del distrito de Molinopampa, provincia de Chachapoyas, región Amazonas. Rev. Indes. 2016, 2, 44–52. [Google Scholar] [CrossRef]
- Finer, M.; Mmani, N. Minería Ilegal baja 78% en la Amazonía Peruana, pero aún Amenaza áreas Clave.MAAP:130. 2020. Available online: https://www.maaproject.org/2020/mineria_ilegal/ (accessed on 12 May 2021).
- Castillo, E.B.; Cayo, E.Y.T.; de Almeida, C.M.; López, R.S.; Briceño, N.B.R.; López, J.O.S.; Gurbillón, M.A.B.; Oliva, M.; Espinoza-Villar, R. Monitoring wildfires in the northeastern peruvian amazon using landsat-8 and sentinel-2 imagery in the GEE platform. ISPRS Int. J. Geo-Inf. 2020, 9, 564. [Google Scholar] [CrossRef]
- Manríquez, H.M. Especies forestales afectadas en incendios ocurridos en Amazonas: Un análisis de la información fiscal de los casos de Chachapoyas y Luya. Arnaldoa 2019, 26, 965–976. [Google Scholar] [CrossRef]
- Nolasco, M.I.M.; León, H. Los Incendios Forestales En El Perú: Grave Problema Por Resolver. Floresta 2004, 34, 179–186. [Google Scholar] [CrossRef] [Green Version]
- Sabuco, P. The problem of forest fires and the basis for its teledetection in Perú. Apunt. Cienc. Soc. 2013, 3, 5–8. [Google Scholar]
- GEOBOSQUES. Bosque y Pérdida de Bosque. Available online: http://geobosques.minam.gob.pe/geobosque/view/perdida.php (accessed on 30 May 2021).
- SERFOR. Lineamientos para la Restauración de Ecosistemas Forestales y Otros Ecosistemas de Vegetación Silvestre; SERFOR: Lima, Peru, 2018. [Google Scholar]
- Cerrón, J.; del Castillo, J.D.; Thomas, E.; Mathez-Stiefel, S.-L.; Franco, M.; Mamani, A.; Gonzalez, F.B.I. Experiencias de Restauración en el Perú—Lecciones Aprendidads; Servicio Nacional Forestal y de Fauna Silvestre: Lima, Perú, 2018; Available online: http://repositorio.serfor.gob.pe/handle/SERFOR/493 (accessed on 12 July 2021).
- Yalle, S.; McBreen, J. Experiencias de la Restauración del Paisaje Forestal con AplicaCión de ROAM en Perú; Quito, Ecuador, 2018; Available online: https://infoflr.org/sites/default/files/2020-04/flr_peru_experiencias_roam.pdf (accessed on 12 March 2021).
- Meza, A.; Sabogal, C.; de Jong, W. Rehabilitacion de areas Degradadas en la Amazonia Peruana: Revision de Experiencias y Lecciones Aprendidas; Bogor, Indonesia, 2006; Available online: https://www.cifor.org/publications/pdf_files/Books/BMeza0601.pdf (accessed on 12 February 2021).
- Núñez, E.; De la Cruz, H.; Proaño, R. Buenas Prácticas para la Recuperación de Pastizales de Altura; CONDESAN: Lima, Peru, 2018; Available online: https://condesan.org/wp-content/uploads/2018/10/Buenas-practicas-Pastizales-22-marzo.pdf (accessed on 29 February 2021).
- Prăvălie, R.; Patriche, C.; Săvulescu, I.; Sîrodoev, I.; Bandoc, G.; Sfîcă, L. Spatial assessment of land sensitivity to degradation across Romania. A quantitative approach based on the modified MEDALUS methodology. Catena 2020, 187, 104407. [Google Scholar] [CrossRef]
- Walentowski, H.; Heinrichs, S.; Hohnwald, S.; Wiegand, A.; Heinen, H.; Thren, M.; Gamarra Torres, O.A.; Sabogal, A.B.; Zerbe, S. Vegetation succession on degraded sites in the Pomacochas Basin (Amazonas, N Peru)-Ecological options for forest restoration. Sustainability 2018, 10, 609. [Google Scholar] [CrossRef] [Green Version]
- Leiva-Tafur, D.; Goñas, M.; Culqui, L.; Santa Cruz, C.; Rascón, J.; Oliva-Cruz, M. Spatiotemporal distribution of physicochemical parameters and toxic elements in Lake Pomacochas, Amazonas, Peru. Front. Environ. Sci. 2022, 10, 1822. [Google Scholar] [CrossRef]
- Rascón, J.; Corroto, F.; Leiva-Tafur, D.; Gamarra Torres, O.A. Variaciones limnológicas espaciotemporales de un lago altoandino tropical al norte de Perú. Ecol. Austral 2021, 31, 343–356. [Google Scholar] [CrossRef]
- Escobedo, R. Fisiografía; Proyecto Zonificación Ecológica y Económica del Departamento de Amazonas, Convenio Entre el IIAP y el Gobierno Regional de Amazonas: Iquitos, Peru, 2010. [Google Scholar]
- MINAM. Mapa Nacional de Cobertura Vegetal; MINAM: Lima, Peru, 2015. Available online: https://www.minam.gob.pe/patrimonio-natural/wp-content/uploads/sites/6/2013/10/MAPA-NACIONAL-DE-COBERTURA-VEGETAL-FINAL.compressed.pdf (accessed on 28 April 2022).
- Escobedo, R. Suelo y Capacidad de Uso Mayor de la Tierra, Informe Temático; Proyecto Zonificación Ecológica y Económica del Departamento de Amazonas, Convenio Entre el IIAP y el Gobierno Regional de Amazonas: Iquitos, Peru, 2010. [Google Scholar]
- INEI. Peru: Crecimiento y Distribucion de la Poblacion Total, 2017. Poblacion Censada Mas Poblacion Omitida; INEI: Lima, Peru, 2018.
- Oliva, M.; Oliva, C.; Rojas, D.; Oliva, M.; Morales, A. Botanical identification of native species most important of dairy basins Molinopampa, Pomacochas and Leymebamba, Amazonas, Peru. Sci. Agropecu. 2015, 6, 125–129. [Google Scholar] [CrossRef] [Green Version]
- Chávez, H.; Leiva, D.; Rascón, J.; Hoyos, I.; Corroto, F. Estado trófico del lago Pomacochas a través de parámetros fisicoquímicos y bacteriológicos. Indes 2016, 2, 98–107. [Google Scholar] [CrossRef]
- Rascón, J.; Corroto, F. Evolución fisicoquímica y de las bacterias del azufre en microcosmos de diferentes sistemas acuáticos de la Región Amazonas. Tayacaja 2020, 3, 25–39. [Google Scholar] [CrossRef]
- Farr, T.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; et al. Shuttle Radar Topography Mission: Mission to map the world. Rev. Geophys. 2007, 45, 3–5. [Google Scholar] [CrossRef] [Green Version]
- Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
- Castro, W.F. Geología; Proyecto Zonificación Ecológica y Económica del Departamento de Amazonas, Convenio Entre el IIAP y el Gobierno Regional de Amazonas: Iquitos, Peru, 2010. [Google Scholar]
- Fidel, L.; Villacorta, S.; Zavala, B.; Vilchez, M.; Valderrama, P.; Nuñez, S.; Luque, G.; Rosado, M.; Medina, L.; Vásquez, J. Mapa de susceptibilidad por movimientos en masa del Perú. Rev. Asoc. Geol. Argent. 2010, 3, 308–311. [Google Scholar]
- Sisodia, P.S.; Tiwari, V.; Kumar, A. Analysis of Supervised Maximum Likelihood Classification for remote sensing image. In Proceedings of the International Conference on Recent Advances and Innovations in Engineering (ICRAIE 2014), Jaipur, India, 9–11 May 2014; pp. 9–12. [Google Scholar] [CrossRef]
- Thakkar, A.K.; Desai, V.R.; Patel, A.; Potdar, M.B. Post-classification corrections in improving the classification of Land Use/Land Cover of arid region using RS and GIS: The case of Arjuni watershed, Gujarat, India. Egypt. J. Remote Sens. Space Sci. 2017, 20, 79–89. [Google Scholar] [CrossRef] [Green Version]
- MINAGRI. Aprueban el Reglamento para la Ejecución de Levantamiento de Suelos. Available online: https://www.minagri.gob.pe/portal/download/pdf/marcolegal/normaslegales/decretossupremos/2010/ds13-2010-ag.pdf (accessed on 24 April 2021).
- Varouchakis, E.A. Geostatistics: Mathematical and Statistical Basis; Elsevier Inc.: Amsterdam, The Netherlands, 2019; ISBN 9780128116890. [Google Scholar]
- Zhang, S.; Huang, Y.; Shen, C.; Ye, H.; Du, Y. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma 2012, 171–172, 35–43. [Google Scholar] [CrossRef]
- Peng, G.; Bing, W.; Guangpo, G.; Guangcan, Z. Spatial distribution of soil organic carbon and total nitrogen based on GIS and geostatistics in a small watershed in a hilly area of northern China. PLoS ONE 2013, 8, e83592. [Google Scholar] [CrossRef]
- Yao, X.; Yu, K.; Deng, Y.; Liu, J.; Lai, Z. Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China. J. For. Res. 2020, 31, 2385–2394. [Google Scholar] [CrossRef] [Green Version]
- Cao, S.; Lu, A.; Wang, J.; Huo, L. Modeling and mapping of cadmium in soils based on qualitative and quantitative auxiliary variables in a cadmium contaminated area. Sci. Total Environ. 2017, 580, 430–439. [Google Scholar] [CrossRef] [PubMed]
- Chabala, L.M.; Mulolwa, A.; Lungu, O. Application of Ordinary Kriging in Mapping Soil Organic Carbon in Zambia. Pedosphere 2017, 27, 338–343. [Google Scholar] [CrossRef]
- Guerrero, J. Clases Texturales de Suelo Utilizando el Raster Calculator de QGIS. Available online: https://joseguerreroa.wordpress.com/2015/04/06/clases-texturales-de-suelo-utilizando-el-raster-calculator-de-qgis/ (accessed on 6 May 2021).
- Hosseini, S.M.; Sadrafshari, S.; Fayzolahpour, M. Desertification hazard zoning in Sistan Region, Iran. J. Geogr. Sci. 2012, 22, 885–894. [Google Scholar] [CrossRef]
- Salvati, R.; Salvati, L.; Corona, P.; Barbati, A.; Ferrara, A. Estimating the sensitivity to desertification of Italian forests. IForest 2015, 8, 287–294. [Google Scholar] [CrossRef] [Green Version]
- Lahlaoi, H.; Rhinane, H.; Hilali, A.; Lahssini, S.; Moukrim, S. Desertification assessment using MEDALUS model in watershed Oued El Maleh, Morocco. Geosciences 2017, 7, 50. [Google Scholar] [CrossRef] [Green Version]
- Kosmas, C.; Kairis, O.; Karavitis, C.; Ritsema, C.; Salvati, L.; Acikalin, S.; Alcalá, M.; Alfama, P.; Atlhopheng, J.; Barrera, J.; et al. Evaluation and Selection of Indicators for Land Degradation and Desertification Monitoring: Methodological Approach. Environ. Manag. 2014, 54, 951–970. [Google Scholar] [CrossRef] [Green Version]
- Xu, D.; You, X.; Xia, C. Assessing the spatial-temporal pattern and evolution of areas sensitive to land desertification in North China. Ecol. Indic. 2019, 97, 150–158. [Google Scholar] [CrossRef]
- Lee, E.J.; Piao, D.; Song, C.; Kim, J.; Lim, C.H.; Kim, E.; Moon, J.; Kafatos, M.; Lamchin, M.; Jeon, S.W.; et al. Assessing environmentally sensitive land to desertification using MEDALUS method in Mongolia. For. Sci. Technol. 2019, 15, 210–220. [Google Scholar] [CrossRef] [Green Version]
- Salvati, L.; Mancino, G.; De Zuliani, E.; Sateriano, A.; Zitti, M.; Ferrara, A. An expert system to evaluate environmental sensitivity: A local-scale approach to desertification risk. Appl. Ecol. Environ. Res. 2013, 11, 611–627. [Google Scholar] [CrossRef]
- UNEP. World Atlas of Desertification; The United Nations Environment Programme (UNEP): London, UK, 1993. [Google Scholar]
- Arnoldus, H.M.J. An Approximation of the Rainfall Factor in the Universal Soil Loss Equation; Land and Water Development Division, FAO: Rome, Italy, 1980; Available online: https://www.cabdirect.org/cabdirect/abstract/19831974087 (accessed on 13 July 2021).
- Li, P.; Li, Z. Soil reinforcement by a root system and its effects on sediment yield in response to concentrated flow in the loess plateau. Agric. Sci. 2011, 2, 86–93. [Google Scholar] [CrossRef] [Green Version]
- Gyssels, G.; Poesen, J.; Bochet, E.; Li, Y. Impact of plant roots on the resistance of soils to erosion by water: A review. Prog. Phys. Geogr. 2005, 29, 189–217. [Google Scholar] [CrossRef] [Green Version]
- Dai, L.; Wang, L.; Liang, T.; Zhang, Y.; Li, J.; Xiao, J.; Dong, L.; Zhang, H. Geostatistical analyses and co-occurrence correlations of heavy metals distribution with various types of land use within a watershed in eastern QingHai-Tibet Plateau, China. Sci. Total Environ. 2019, 653, 849–859. [Google Scholar] [CrossRef] [PubMed]
- Keesstra, S.; Mol, G.; de Leeuw, J.; Okx, J.; Molenaar, C.; de Cleen, M.; Visser, S. Soil-related sustainable development goals: Four concepts to make land degradation neutrality and restoration work. Land 2018, 7, 133. [Google Scholar] [CrossRef] [Green Version]
- Keesstra, S.D.; Bouma, J.; Wallinga, J.; Tittonell, P.; Smith, P.; Cerdà, A.; Montanarella, L.; Quinton, J.N.; Pachepsky, Y.; Van Der Putten, W.H.; et al. The significance of soils and soil science towards realization of the United Nations sustainable development goals. Soil 2016, 2, 111–128. [Google Scholar] [CrossRef] [Green Version]
- MIDAGRI. Plan Nacional de Desarrollo Ganadero 2017–2027. Lima, Peru. Available online: https://www.midagri.gob.pe/portal/download/pdf/especiales/plan-nacional-ganadero.pdf (accessed on 30 September 2022).
- Murga, L.; Vásquez, H.; Bardales, J. Caracterización de los sistemas de producción de ganado bovino en las cuencas ganaderas de Ventilla, Florida y Leyva -región Amazonas. Rev. Cient. Cienc. Nat. Ing. 2019, 1, 28–37. [Google Scholar] [CrossRef]
- Cole, R.J.; Holl, K.D.; Zahawi, R.A. Seed rain under tree islands planted to restore degraded lands in a tropical agricultural landscape. Ecol. Appl. 2010, 20, 1255–1269. [Google Scholar] [CrossRef]
- Rojas, N.B.; Cotrina, D.A.; Barboza, E.; Barrena, M.A.; Sarmiento, F.O.; Sotomayor, D.A.; Oliva, M.; Salas, R. Current and future distribution of five timber forest species in amazonas, northeast peru: Contributions towards a restoration strategy. Diversity 2020, 12, 305. [Google Scholar] [CrossRef]
- Cotrina, D.A.; Barboza, E.; Rojas, N.B.; Oliva, M.; Torres, C.; Amasifuen, C.A.; Bandopadhyay, S. Distribution models of timber species for forest conservation and restoration in the Andean-Amazonian landscape, North of Peru. Sustainability 2020, 12, 7945. [Google Scholar] [CrossRef]
- Lamb, D.; Erskine, P.D.; Parrotta, J.A. Restoration of degraded tropical forest landscapes. Science 2005, 310, 1628–1632. [Google Scholar] [CrossRef] [Green Version]
- Joseph, N.; Orefice, J.C. Silvopasture—It’s Not a Load of Manure: Differentiating between Silvopasture and Wooded Livestock Paddocks in the Northeastern United States. J. For. 2017, 115, 71–72. [Google Scholar] [CrossRef]
- Orefice, J.; Smith, R.G.; Carroll, J.; Asbjornsen, H.; Howard, T. Forage productivity and profitability in newly-established open pasture, silvopasture, and thinned forest production systems. Agrofor. Syst. 2019, 93, 51–65. [Google Scholar] [CrossRef]
- Ibrahim, M.; Guerra, L.; Casasola, F.; Neely, C. Importance of silvopastoral systems for mitigation of climate change and harnessing of environmental benefits. Integr. Crop Manag. 2010, 11, 189–196. [Google Scholar]
- Mercer, D.E.; Frey, G.E.; Cubbage, F.W. Economics of Agroforestry. In Handbook of Forest Resource Economics; 2014; Available online: https://www.taylorfrancis.com/chapters/edit/10.4324/9780203105290-19/economics-agroforestry-evan-mercer-gregory-frey-frederick-cubbage (accessed on 17 April 2021).
- Chizmar, S.; Castillo, M.; Pizarro, D.; Vasquez, H.; Bernal, W.; Rivera, R.; Sills, E.; Abt, R.; Parajuli, R.; Cubbage, F. A discounted cash flow and capital budgeting analysis of silvopastoral systems in the Amazonas region of Peru. Land 2020, 9, 353. [Google Scholar] [CrossRef]
- Prasad, R.; Hochmuth, G.; Wilkie, A.C. Anaerobic Digesters for Manure Management at Livestock Operations; University of Florida, Institute of Food and Agricultural Sciences: Gainesville, FL, USA, 2014; Available online: https://edis.ifas.ufl.edu/publication/SS615 (accessed on 23 April 2021).
- Galindo-Barboza, A.J.; Domínguez-Araujo, G.; Arteaga-Garibay, R.I.; Salazar-Gutiérrez, G. Mitigation and adaptation to climate change through the implementation of integrated models for the management and use of livestock residues. Rev. Mex. Cienc. Pecu. 2018, 11, 107–125. [Google Scholar] [CrossRef]
- Martí-Herrero, J. Biodigestores Familiares: Guía de diseño y manual de instalación. 2008. Available online: https://www.researchgate.net/publication/282156621_Biodigestores_familiares_Guia_de_diseno_y_manual_de_instalacion_2008?channel=doi&linkId=5605833408aeb5718ff1c295&showFulltext=true (accessed on 29 April 2021).
- Kögel-Knabner, I.; Amelung, W. Soil organic matter in major pedogenic soil groups. Geoderma 2021, 384, 114785. [Google Scholar] [CrossRef]
- Jurgensen, M.F.; Harvey, A.E.; Graham, R.T.; Page-Dumroese, D.S.; Tonn, J.R.; Larsen, M.J.; Jain, T.B. Impacts of timber harvesting on soil organic matter, nitrogen, productivity, and health of Inland Northwest forests. For. Sci. 1997, 43, 234–251. [Google Scholar] [CrossRef]
- González-Pérez, J.A.; González-Vila, F.J.; Almendros, G.; Knicker, H. The effect of fire on soil organic matter—A review. Environ. Int. 2004, 30, 855–870. [Google Scholar] [CrossRef]
- Fan, M.; Lal, R.; Zhang, H.; Margenot, A.J.; Wu, J.; Wu, P.; Zhang, L.; Yao, J.; Chen, F.; Gao, C. Variability and determinants of soil organic matter under different land uses and soil types in eastern China. Soil Tillage Res. 2020, 198, 104544. [Google Scholar] [CrossRef]
- de Andrade Bonetti, J.; Anghinoni, I.; de Moraes, M.T.; Fink, J.R. Resilience of soils with different texture, mineralogy and organic matter under long-term conservation systems. Soil Tillage Res. 2017, 174, 104–112. [Google Scholar] [CrossRef]
- Bouabid, R.; Rouchdi, M.; Badraoui, M.; Diab, A.; Louafi, S. Assessment of Land Desertification Based on the MEDALUS Approach and Elaboration of an Action Plan: The Case Study of the Souss River Basin, Morocco. In Land Degradation and Desertification: Assessment, Mitigation and Remediation; 2010; Available online: https://link.springer.com/chapter/10.1007/978-90-481-8657-0_10 (accessed on 3 May 2021).
- Young, K.R.; Leon, B. Biodiversity conservation in Peru’s eastern Montane forests. Mt. Res. Dev. 2000, 20, 208–211. [Google Scholar] [CrossRef]
- Fletcher, W.D.; Smith, C.B. Reaching Net Zero: What it Takes to Solve the Global Climate Crisis; Elsevier: Amsterdam, The Netherlands, 2020; Available online: https://www.sciencedirect.com/book/9780128233665/reaching-net-zero (accessed on 8 July 2021).
- Fernandez, J.P.R.; Franchito, S.H.; Rao, V.B. Future Changes in the Aridity of South America from Regional Climate Model Projections. Pure Appl. Geophys. 2019, 176, 2719–2728. [Google Scholar] [CrossRef]
- Cheval, S.; Dumitrescu, A.; Birsan, M.V. Variability of the aridity in the South-Eastern Europe over 1961–2050. Catena 2017, 151, 74–86. [Google Scholar] [CrossRef]
- Román, F.; Mamani, A.; Cruz, A.; Sandoval, C.; Cuesta, F. Orientaciones para la Restauración de Ecosistemas Forestales y otros Ecosistemas de Vegetación Silvestre; Servicio Nacional Forestal y de Fauna Silvestre (SERFOR): Lima, Peru, 2018; Available online: http://repositorio.serfor.gob.pe/bitstream/SERFOR/524/1/SERFOR%202018%20Orientaciones-para-larestauraci%C3%B3n-de-ecosistemas-forestales.pdf (accessed on 30 September 2022).
- Flores, Y. Especies Forestales Nativas para la Recuperacion de Areas Degradadas; Instituto Nacional de Innovación Agraria: Lima, Peru, 2014; Available online: http://repositorio.inia.gob.pe/handle/20.500.12955/473 (accessed on 30 September 2022).
- Schmidt, I.B.; de Urzedo, D.I.; Piña-Rodrigues, F.C.M.; Vieira, D.L.M.; de Rezende, G.M.; Sampaio, A.B.; Junqueira, R.G.P. Community-based native seed production for restoration in Brazil—The role of science and policy. Plant Biol. 2019, 21, 389–397. [Google Scholar] [CrossRef] [PubMed]
- Romijn, E.; Coppus, R.; De Sy, V.; Herold, M.; Roman-Cuesta, R.M.; Verchot, L. Land restoration in Latin America and the Caribbean: An overview of recent, ongoing and planned restoration initiatives and their potential for climate change mitigation. Forests 2019, 10, 510. [Google Scholar] [CrossRef]
Parameters | Description | Classes | Quality Scores |
---|---|---|---|
Parent material | Pucara group | Low | 1.55 |
Mitu group | Medium | 1.60 | |
Sarayaquillo formation, Goyllarisquizga group, and Chulec formation | 1.65 | ||
Chonta formation | High | 1.70 | |
Slope | <2 Nearly level | Low | 1 |
2–6 Gentling sloping | Low | 1.2 | |
6–12 Moderately sloping | Medium | 1.4 | |
12–18 Strongly sloping | Medium | 1.6 | |
18–25 Moderately steep | High | 1.7 | |
25–35 Steep | High | 1.8 | |
35–60 Very steep | High | 1.9 | |
>60 Very steep | High | 2 | |
Organic matter content | >6.0 | High | 1 |
2.1–6.0 | Medium | 1.3 | |
2.0–1.1 | Low | 1.6 | |
<1.0 | Very low | 2 | |
Soil texture | Loam, Sandy slay loam, Sandy loam, Loamy sand, Clay loam good | Very Low | 1 |
Sandy clay, Silt loam, Silty clay loam moderate | Low | 1.2 | |
Silt, And clay, Silty clay poor | Medium | 1.6 | |
Sand very poor | High | 2 |
Parameters | Description | Classes | Quality Scores |
---|---|---|---|
Aridity index | <0.05 Hyper-arid zone | Very high | 2 |
0.05–0.2 Arid | High | 1.8 | |
0.2–0.5 Semiarid | Medium | 1.60 | |
0.5–0.65 Dry subhumid | Medium | 1.4 | |
0.65–1 Subhumid | Low | 1.2 | |
>1 Humid | Very low | 1 | |
Erosivity | 0–60 Very low | Very low | 1 |
60–90 Low | Low | 1.2 | |
90–120 Moderate | Medium | 1.5 | |
120–160 Severe | High | 1.8 | |
>160 Very severe | Very high | 2 | |
Aspect | North, Northwest, Northeast, West, flat areas | Low | 1 |
South, Southwest, Southeast, East | High | 2 |
Parameters | Description | Classes | Quality Scores |
---|---|---|---|
Drought resistance | Forests | Very low | 1 |
Grasslands | Medium | 1.4 | |
Pastures and crops | High | 1.7 | |
Rainfed crops; bare floors | Very High | 2 | |
Erosion protection | Forests | Very low | 1 |
Pastures and crops | Low | 1.3 | |
Grasslands | Medium | 1.6 | |
Rainfed crops; bare floors | High | 2 | |
Vegetation cover | Forests, Grasslands | Low | 1 |
Pastures and crops | Medium | 1.8 | |
Rainfed crops; bare floors | High | 2 |
Parameters | Description | Classes | Quality Scores |
---|---|---|---|
Agricultural intensity | Forests | Low | 1 |
Grasslands, pastures and crops | Moderate | 1.5 | |
Rainfed crops; bare floors | High | 2 | |
Landslides | Stable terrain | Very low | 1 |
Conditionally stable slope | Low | 1.4 | |
Fossil landslides | Medium | 1.5 | |
Dormant landslides | 1.6 | ||
Active landslides with dormant sliding process | High | 1.8 | |
Active landslides with present sliding process | Very high | 2 |
Indicator | Quality Description | Range of Scores | Total Area (km2) | % |
---|---|---|---|---|
SQI | High | <1.13 | 4.8 | 2.16 |
Moderate | 1.13–1.45 | 197.07 | 88.61 | |
Low | >1.46 | 15.54 | 6.99 | |
CQI | High | <1.15 | 115.97 | 52.14 |
Moderate | 1.15–1.81 | 101.44 | 45.61 | |
Low | >1.81 | - | - | |
VQI | High | <1.13 | 101.03 | 45.43 |
Moderate | 1.13–1.38 | 113.16 | 50.88 | |
Low | >1.38 | 3.22 | 1.45 | |
MQI | High | 1–1.25 | 118.37 | 53.22 |
Moderate | 1.26–1.50 | 97.17 | 43.69 | |
Low | >1.51 | 1.88 | 0.84 | |
Urban area | 0.74 | 0.33 | ||
Lake Pomacochas | 4.25 | 1.91 |
Class | Sub Class | Range of Scores | Total Area (km2) | % |
---|---|---|---|---|
Non-affected | N | <1.17 | 67.74 | 30.46 |
Potential | P | 1.17–1.22 | 34.43 | 15.48 |
Fragile | F1 | 1.23–1.26 | 41.72 | 18.76 |
F2 | 1.27–1.32 | 48.38 | 21.75 | |
F3 | 1.33–1.37 | 18.56 | 8.34 | |
Critical | C1 | 1.38–1.41 | 3.47 | 1.56 |
C2 | 1.42–1.53 | 2.68 | 1.21 | |
C3 | >1.53 | 0.44 | 0.20 | |
Urban area | 0.74 | 0.33 | ||
Lake Pomacochas | 4.25 | 1.91 |
Indices | SQI | CQI | VQI | MQI | ESAI |
---|---|---|---|---|---|
SQI | 1 | −0.11 | 0.53 | 0.78 | 0.86 |
CQI | −0.11 | 1 | −0.46 | −0.27 | 0.05 |
VQI | 0.53 | −0.46 | 1 | 0.85 | 0.79 |
MQI | 0.78 | −0.27 | 0.85 | 1 | 0.93 |
ESAI | 0.86 | 0.05 | 0.79 | 0.93 | 1 |
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Meza Mori, G.; Torres Guzmán, C.; Oliva-Cruz, M.; Salas López, R.; Marlo, G.; Barboza, E. Spatial Analysis of Environmentally Sensitive Areas to Soil Degradation Using MEDALUS Model and GIS in Amazonas (Peru): An Alternative for Ecological Restoration. Sustainability 2022, 14, 14866. https://doi.org/10.3390/su142214866
Meza Mori G, Torres Guzmán C, Oliva-Cruz M, Salas López R, Marlo G, Barboza E. Spatial Analysis of Environmentally Sensitive Areas to Soil Degradation Using MEDALUS Model and GIS in Amazonas (Peru): An Alternative for Ecological Restoration. Sustainability. 2022; 14(22):14866. https://doi.org/10.3390/su142214866
Chicago/Turabian StyleMeza Mori, Gerson, Cristóbal Torres Guzmán, Manuel Oliva-Cruz, Rolando Salas López, Gladys Marlo, and Elgar Barboza. 2022. "Spatial Analysis of Environmentally Sensitive Areas to Soil Degradation Using MEDALUS Model and GIS in Amazonas (Peru): An Alternative for Ecological Restoration" Sustainability 14, no. 22: 14866. https://doi.org/10.3390/su142214866