Geotechnologies on the phosphorus stocks determination in tropical soils: General impacts on society.

Phosphorus (P) is a critical nutrient for primary production in terrestrial and aquatic ecosystems. As P mineral reserves are finite and non-renewable, there is an increasing discussion on its sustainable utilization to safeguard food security for future generations. Understanding the spatial distribution of soil P is central in advancing effective phosphorus management and fostering sustainable agricultural practices. This study aims to digitally map the stocks of available P (AP) and total P (TP) in Brazil at a fine resolution (30 m). Using the Random Forest machine learning algorithm and a database of topsoil (0-20 cm) with 28,572 samples for AP and 3154 for TP, we predicted P stocks based on environmental covariates related to soil formation processes. By dividing Brazil into two sub-regions, representing areas with native coverage and anthropogenic ones, we built independent predictive models for each sub-region. Our results show that Brazil has a TP stock of 531 Tg and an AP stock of 17.4 Tg. The largest soil TP stocks are in the Atlantic Forest biome (73.8 g.m2), likely due to higher organic carbon stocks in this biome. The largest AP stocks were in the Caatinga biome (2.51 g.m2) because of younger soils with low P adsorption capacity. We also found that fertilizer use significantly increased AP stocks in agricultural areas compared to native ones. Our results indicated that AP stocks strongly influenced Brazil's agricultural production, with a correlation coefficient ranging from 0.20 for coffee crops to 0.46 for soybean. The maps generated in this study are expected to contribute to the sustainable use of P in agriculture and environmental systems.

A soil productivity system reveals most Brazilian agricultural lands are below their maximum potential.

Food production is extremely dependent on the soil. Brazil plays an important role in the global food production chain. Although only 30% of the total Brazilian agricultural areas are used for crop and livestock, the full soil production potential needs to be evaluated due to the environmental and legal impossibility to expand agriculture to new areas. A novel approach to assess the productive potential of soils, called "SoilPP" and based on soil analysis (0-100 cm) - which express its pedological information - and machine learning is presented. Historical yields of sugarcane and soybeans were analyzed, allowing to identify where it is still possible to improve crop yields. The soybean yields were below the estimated SoilPP in 46% of Brazilian counties and could be improved by proper management practices. For sugarcane, 38% of areas can be improved. This technique allowed us to understand and map the food yield situation over large areas, which can support farmers, consultants, industries, policymakers, and world food security planning.

Remote sensing imagery detects hydromorphic soils hidden under agriculture system.

The pressure for food production has expanded agriculture frontiers worldwide, posing a threat to water resources. For instance, placing crop systems over hydromorphic soils (HS), have a direct impact on groundwater and influence the recharge of riverine ecosystems. Environmental regulations improved over the past decades, but it is difficult to detect and protect these soils. To overcome this issue, we applied a temporal remote sensing strategy to generate a synthetic soil image (SYSI) associated with random forest (RF) to map HS in an 735,953.8 km2 area in Brazil. HS presented different spectral patterns from other soils, allowing the detection by satellite sensors. Slope and SYSI contributed the most for the prediction model using RF with cross validation (accuracy of 0.92). The assessments showed that 14.5% of the study area represented HS, mostly located inside agricultural areas. Soybean and pasture areas had up to 14.9% while sugar cane had just 3%. Here we present an advanced remote sensing technique that may improve the identification of HS under agriculture and assist public policies for their conservation.

Digital mapping of the soil available water capacity: tool for the resilience of agricultural systems to climate change.

Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. Digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties. DSM techniques commonly apply machine learning (ML) models, with a high level of complexity. In this context, we aimed to perform a digital mapping of soil AWC and interpret the results of the Random Forest (RF) algorithm and, in a case study, to show that digital AWC maps can support agricultural planning in response to the local effects of climate change. To do so, we divided this research into two approaches: In the first approach, we showed a DSM using 1857 sample points in a southeastern region of Brazil with laboratory-determined soil attributes, together with a pedotransfer function (PTF), remote sensing and DSM techniques. In the second approach, the constructed AWC digital soil map and weather station data were used to calculate climatological soil water balances for the periods between 1917-1946 and 1991-2020. The result showed the selection of covariates using Shapley values as a criterion contributed to the parsimony of the model, obtaining goodness-of-fit metrics of R2 0.72, RMSE 16.72 mm m-1, CCC 0.83, and Bias of 0.53 over the validation set. The highest contributing covariates for soil AWC prediction were the Landsat multitemporal images with bare soil pixels, mean diurnal, and annual temperature range. Under the current climate conditions, soil available water content (AW) increased during the dry period (April to August). May had the highest increase in AW (∼17 mm m-1) and decrease in September (∼14 mm m-1). The used methodology provides support for AWC modeling at 30 m resolution, as well as insight into the adaptation of crop growth periods to the effects of climate change.

A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics.

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0-20 cm) in a 2574 km2 agricultural area located in São Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (-0.26 > r > -0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.