Multicriteria spatial model to prioritize degraded areas for landscape restoration through agroforestry.

Reconciling the restoration of ecosystem services within agricultural landscapes is an effort that has been advancing within degraded areas restoration through agroforestry systems. However, to contribute to the effectiveness of these initiatives, it is essential to integrate landscape vulnerability and local demands to better highlight in which areas the implementation of agroforestry systems should be prioritized. Thus, we developed a spatial hierarchization methodology as a decision support tool as an active strategy for agroecosystem restoration. The proposed method constitutes a spatial indicator of priority areas to guide agroforestry interventions, including resource allocation and public policies for payment for environmental services. The methodology consists of Multicriteria Decision Analysis implemented in GIS software by combining input datasets based on biophysical conditions, environmental and socioeconomic aspects, that integrated promotes an assessment of the environment fragility, the pressures and responses to land use dynamic; a strategy for landscape restoration and conservation of the natural habitats, and multiple specific scenarios for decision making regarding the agricultural and the local actors demands. The output of the model provides the spatial distribution of areas suitable for the implementation of agroforestry systems, sorted into four priority levels (Low, Medium, High, and Extreme priority). The method is a promising tool proposal for territorial management and governance and subsidizes future research on the flows of ecosystem services.•Assessment of the environment fragility and the pressures and responses to land use dynamic.•Strategy for landscape restoration and conservation of remaining natural habitats.•Multiple specific scenarios for decision making regarding the agricultural and the local actors demands.

High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems.

The detection of Solar-Induced chlorophyll Fluorescence (SIF) by remote sensing has opened new perspectives on ecosystem studies and other related aspects such as photosynthesis. In general, fluorescence high-resolution studies were limited to proximal sensors, but new approaches were developed to improve SIF resolution by combining OCO-2 with MODIS orbital observations, improving its resolution from 0.5° to 0.05 on a global scale. Using a high-resolution dataset and rainfall data some SIF characteristics of the satellite were studied based across 06 contrasting ecosystems in Brazil: Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal, from years 2015-2018. SIF spatial variability in each biome presented significant spatial variability structures with high R2 values (>0.6, Gaussian models) in all studied years. The rainfall maps were positively and similar related to SIF spatial distribution and were able to explain more than 40% of SIF's spatial variability. The Amazon biome presented the higher SIF values (>0.4 W m-2 sr-1 µm-1) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m-2 sr-1 µm-1, precipitation around 500 mm). The linear relationship of SIF to rainfall across biomes was mostly significant (except in Pantanal) and presented contrasting sensitivities as in Caatinga SIF was mostly affected while in the Amazon, SIF was lesser affected by precipitation events. We believe that the features presented here indicate that SIF could be highly affected by rainfall precipitation changes in some Brazilian biomes. Combining rainfall with SIF allowed us to detect the differences and similarities across Brazil's biomes improving our understanding on how these ecosystems could be affected by climate change and severe weather conditions.

Spatial indicator of priority areas for the implementation of agroforestry systems: An optimization strategy for agricultural landscapes restoration.

The ecological functions restoration in agricultural areas is a major challenge on a landscape scale. In the specific case of active restoration through Agroforestry Systems (AFS), the absence of a specific direction hinders ecological restoration processes, especially in regions that prefer intensive agriculture. Thus, this study aims to develop a Spatial Indicator of Priority Areas to guide Agroforestry Systems implementation in agricultural landscapes. A spatial multicriteria decision analysis (MDCA) was carried out based on environmental factors: soil, geology and slope (which determine the natural vulnerability of the land) and anthropogenic factors: land use and land cover, forest fragments, potential land use capacity and legal protected areas in rural properties (which reflects human pressure and land use suitability). Subsequently, four priority levels were classified for agroforestry interventions: (1) Low priority; (2) Average priority; (3) High priority; (4) Extreme priority. A final map was made to identify priority areas for landscape recovery in 9 cities located at the mouth of the Mogi Guaçu River Hydrographic Basin, State of São Paulo, Brazil. Considering the natural vulnerability of the land and the multifunctional aspects of the landscape, the scenarios projection allowed a consensus for forest conservation and agricultural suitability perspectives. A final combination of the explored aspects culminated in the spatial indicator, which model foresees 22,300 ha available for urgent actions for restoration, reforestation and sustainable exploitation through agroforestry systems. We emphasize the challenges in reconciling the socioeconomic and ecological functions in the agroecosystem, however, the metric provides a more inclusive and assertive management strategy for natural resources and advances towards the goal of reforestation and implementation of payment for environmental services (PES) schemes.