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• Agroforestry complexity and soil texture influence ecosystem service provision. • P and Ca cycling through litter and stable soil carbon stocks are synergistic. • Sandy soils are most suited for complex agroforestry. • Agroforestry Complexity Index encompassing diversity, density and management. Agroforestry systems are often promoted as solutions to address land degradation and climate change. However, agroforestry is an umbrella term for a large variety of systems and it is not clear how their degree of complexity influences their provision of soil-based ecosystem services, such as soil organic carbon (SOC) storage and nutrient cycling. Furthermore, a knowledge gap remains whether agroforestry systems perform equally well on all soil types. The objectives of this study were 1) to assess the links between agroforestry complexity, nutrient cycling and SOC fractions, and 2) to assess how soil texture influences these relationships in Brazilian agroforestry systems. We sampled 59 agroforestry plots across 30 sites in Sao Paulo state, Brazil, and 8 monocrop sites (6 pastures and 2 crop monocultures). The 38 sites represented a soil textural gradient, ranging from very sandy to very clayey (clay content range 25 – 620 g kg−1). An Agroforestry Complexity Index (ACI) was defined based on tree species richness, stem density and pruning management. Nutrient (N, P, K, Ca, Mg) and C contents were determined in litter and soil (0–30 cm depth) samples, and mineral-associated organic C (MAOC) and particulate organic C (POC) in soil samples were assessed as well. ACI was positively associated with C, N, P, Ca and Mg stocks in litter, and these litter nutrients were in turn positively associated with the corresponding soil nutrient stocks. Associations between soil nutrients and MAOC were stronger on sandy soils than on clayey soils, particularly for P, Ca and Cation Exchange Capacity (CEC). For POC, robust relationships with nutrients were only found on sandy soils. Structural Equation Models indicated causal relationships between agroforestry complexity, P and Ca cycling, and MAOC and POC stocks in topsoils. Our results indicate that nutrients effectively cycle from in situ mulch into plant-available soil pools and highlight the synergies between nutrient cycling and stable C stocks that can be achieved in complex agroforestry systems. These synergies seem to be particularly strong on sandy soils (<15 % clay). [ABSTRACT FROM AUTHOR]

• Soil texture can be detected through its emissivity spectra. • Soil emissivity decreases as sand content of the samples increases. • Laboratory and satellite sensors have distinct accuracy in soil attributes modeling. • Qualitative analyses of soil spectra reveal relevant information of its composition. Soil texture and organic carbon (OC) content influence the spectral response. These attributes are relevant for the preservation and proper management of land use in the pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a powerful tool for studying so is, but their analysis using these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The aim of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range (ASTER, Landsat satellites). The study performed qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil, where collected 150 soil samples (0–20 cm depth). Soil texture was determined by the pipette method and OC via dry combustion. Reflectance and emissivity (Ɛ) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image "ASTER_05" from July 15, 2017 was acquired with values of Ɛ. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, TIR, TIR Correlation Index (TIR_CID), and MIR Correlation Index (MIR_CID)), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR region, the models for total sand content and for fine and coarse fractions were good. Models created with ASTER sensor data were not as promising as those with laboratory ones. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors. [ABSTRACT FROM AUTHOR]