Sinergia entre mapeamento digital de solos e modelagem de culturas: influência dos dados do solo na produtividade atingível da cana-de-açúcar

Models of crop production play a key role in food security, predicting future agriculture challenges and supporting the establishment of public policies and sustainable management practices. However, due to the lack of reliable information, especially in developing countries, they have presented limited performance and restrictions for spatially explicit analyses. Thus, the objective of this study was to evaluate the DSM (Digital Soil Mapping) as an alternative to fill the gap of soil data. Our study site is in Southwest of Brazil in a 4,815 km2 area heterogeneous in geology and soil classes. The study were conducted with the following framework: (i) We used a soil survey data, containing 1,125 collected points with auger and 27 profiles and applied equal-spline equations to standardized the soil dataset into depth; (ii) A machine learning (ML) algorithm were used to predict soil attributes and their uncertainties (iii) Pedotransfer functions were performed to obtain soil hydrological properties (iv) DSSAT-Canegro was simulated in a 250m grid to sugarcane planted in October with harvest completing 12 months (v) We compared three levels of soil data source: a soil map (SM) (1:100,000 scale), SoilGrids (SG) and the map of attributes (MA) derived from our ML. Clay was the attribute that obtained the best performance to surface and subsurface (R2=0.70 and 0.59, RMSE= 88.87 and 141 g kg-1) and low uncertainty (40 and 110%). In depth the attributes were reduced in their content and increased uncertainty. Therefore, the MA to be the most reliable source of data, being the one that most resembles field data, presents the best index of agreement (d= 0.8) and confidence coefficient (c=0.74). In addition, a 250m grid allowed the evaluation of the spatial variability of the attainable yield of sugarcane at a regional level. Nitisols achieved higher productivity and shallow soils did not exceed 100 t ha-1 Thus, this work showed the applicability of digital mapping for application in crop modeling. This methodology can be replicated for decision-making at a regional level and also to improve management strategies for agriculture.Os modelos de produção agrícola desempenham um papel fundamental na segurança alimentar, prevendo futuros desafios agrícolas e apoiando o estabelecimento de políticas públicas e práticas de gestão sustentável. Entretanto, devido à falta de informações confiáveis, especialmente nos países em desenvolvimento, eles apresentaram desempenho limitado e restrições para análises espacialmente explícitas. Assim, o objetivo deste estudo foi avaliar o MDS (Mapeamento Digital de Solos) como uma alternativa para preencher a ausência de dados do solo. A região considerada nesse estudo está situada no sudeste do Brasil em uma área de 4.815 km2 que detém enorme heterogeneidade quanto a sua geologia e tipos de solo. Para a realização do estudo as seguintes etapas foram realizadas: (i) Foi usado um conjunto de dados de solo, obtidos a partir de 1.125 tradagens e 27 perfis que foram pradronizados em profundidades por meio de equações de interpolação; (ii) Um algoritmo de aprendizado de máquina (AM) foi usado para predição dos atributos de solo e suas incertezas (iii) Funções de pedotransferência foram realizadas para obter as propriedades hidrológicas do solo (iv) DSSAT/CANEGRO foi simulado em uma grade de 250 m para cana-de-açúcar, com plantio em outubro e colheita completando 12 meses (v) Três níveis de fonte de dados do solo foram comparados: um mapa de solo (MS) (escala 1:100.000), SoilGrids (SG) e o mapa de atributos (MA) derivado de nosso AM. A argila foi o atributo que obteve o melhor desempenho em superfície e subsuperfície (R2 =0,70 e 0,59, RMSE= 88,87 e 141 g kg-1) e baixa incerteza (40 e 110%). Em profundidade, os atributos obtiveram uma redução em seu teor e aumento da incerteza. Portanto, o MA foi a fonte de dados de solo mais confiável, sendo a que mais se assemelha aos dados de campo, apresentando o melhor índice de concordância (d= 0,8) e coeficiente de confiança (c=0,74). Além disso, uma grade de 250 m permitiu a avaliação da variabilidade espacial da produtividade atingível da cana-de-açúcar em nível regional. Os nitossolos alcançaram maior produtividade e os solos rasos não excederam 100 t ha-1. Sendo assim, este trabalho mostrou a aplicabilidade do mapeamento digital para uso na modelagem de culturas. Esta metodologia pode ser replicada no planejamento agrícola em nível regional e aplicações de manejo na agricultura

Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison

Multitemporal collections of satellite images and their products have recently been explored in digital soil mapping. This study aimed to produce a bare soil image (BSI) for the São Paulo State (Brazil) to perform a pedometric analysis for different geographical levels. First, we assessed the potential of the BSI for predicting the surface (0.00-0.20 m) and subsurface (0.80-1.00 m) clay, iron oxides (Fe 2 O 3 ), aluminum (m%) and bases saturation (V%) contents at the state level, which are important properties for soil classification. In this task, legacy soil samples, the BSI and terrain attributes were employed in machine learning. In a second moment, we evaluated the capacity of the BSI for clustering the landscape at the regional level, comparing the predicted patterns with a legacy semi-detailed soil map from a smaller reference site. In the final stage, the predicted soil maps from the state level were investigated at the farm level considering several sites distributed across the São Paulo state. Our results demonstrated that clay and Fe 2 O 3 reached the best prediction performance for both depths at the state level, reaching a RMSE of less than 10 %, RPIQ higher than 1.6 and R 2 of at least 0.41. Additionally, the predicted landscape clusters had a significant association with the main pedological classes, subsurface color, soil mineralogy and texture from the legacy semi-detailed soil map. Illustrative examples at the farm level indicated great capacity of BSI in detecting the variations of soils, which were linked to several soil properties, such as texture, iron content, drainage network, among others. Therefore, this study demonstrates that BSI is valuable information derived from optical Earth Observation data that can contribute to the future of soil survey and mapping in Brazil (PronaSolos)

Drivers of Organic Carbon Stocks in Different LULC History and along Soil Depth for a 30 Years Image Time Series

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data