A segmentation approach to delineate zones for differential nitrogen intervention.

Multi-source and -temporal data integration is expected to support the delineation of within-field management zones that may better conform to unique combinations of crop yield variations. This work addresses the evaluation of zone delineation approaches based on image classification and segmentation methods. An object based segmentation is introduced using ancillary data from multivariate analysis of yield maps. A simple economic evaluation is conducted to compare delineation methods aiming variable-rate Nitrogen applications. Advantages and penalties are suggested for 2, 3, and 4 management zones. Results show that a procedure combining multiresolution, watershed and region grow segmentation algorithms has systematically resulted in greater net worth. It is suggested that segmentation methods have potential application for zone management delineations supporting contiguous patter

Spatially explicit seasonal forecasting using fuzzy spatiotemporal clustering of long-term daily rainfall and temperature data

International audienceA major limitation of statistical forecasts for specific weather station sites is that they are not spatial in the true sense. And while spatial predictions have been studied, their results have indicated a lack of seasonality. Global Circulation Models (GCMs) are spatial, but their spatial resolution is rather coarse. Here we propose spatially explicit seasonal forecasting, based on the Fuzzy Classification of long-term (40 years) daily rainfall and temperature data to create climate memberships over time and location. Data were obtained from weather stations across south-east Australia, covering sub-tropical to arid climate zones. Class memberships were used to produce seasonal predictions using correlations with climate drivers and a regression rules approach. Therefore, this model includes both local climate feedback and the continental drivers. The developed seasonal forecasting model predicts rainfall and temperature reasonably accurately. The final 6-month forecast for average maximum temperature and rainfall produced relative errors of 0.89 and 0.56 and Pearson correlation coefficients of 0.83 and 0.82, respectively

Using deep learning for digital soil mapping

Digital soil mapping (DSM) has been widely used as a cost-effective method for generating soil maps. However, current DSM data representation rarely incorporates contextual information of the landscape. DSM models are usually calibrated using point observations intersected with spatially corresponding point covariates. Here, we demonstrate the use of the convolutional neural network (CNN) model that incorporates contextual information surrounding an observation to significantly improve the prediction accuracy over conventional DSM models. We describe a CNN model that takes inputs as images of covariates and explores spatial contextual information by finding non-linear local spatial relationships of neighbouring pixels. Unique features of the proposed model include input represented as a 3-D stack of images, data augmentation to reduce overfitting, and the simultaneous prediction of multiple outputs. Using a soil mapping example in Chile, the CNN model was trained to simultaneously predict soil organic carbon at multiples depths across the country. The results showed that, in this study, the CNN model reduced the error by 30&thinsp;% compared with conventional techniques that only used point information of covariates. In the example of country-wide mapping at 100&thinsp;m resolution, the neighbourhood size from 3 to 9 pixels is more effective than at a point location and larger neighbourhood sizes. In addition, the CNN model produces less prediction uncertainty and it is able to predict soil carbon at deeper soil layers more accurately. Because the CNN model takes the covariate represented as images, it offers a simple and effective framework for future DSM models.</p

Revisão sobre funções de Pedotransferência (PTFs) e novos métodos de predição de classes e atributos do solo.

Revisão sobre o uso das funções de pedotransferência e discussão sobre os vários tipos de PTFs. Diferentes abordagens e alguns princípios são considerados para desenvolver PTFs. Um conceito de sistema de inferência de solo é proposto (SINFERS), em que funções de pedotransferência são as regras do conhecimento, para serem usadas como ferramentas de inferência. É fornecida extensa bibliografia para consulta e expansão do conhecimento e uso da metodologia de pedotransferência.bitstream/CNPS/11589/1/pedotransferencia.pd

Human-induced changes in Indonesian peatlands increase drought severity

Indonesian peatlands are critical to the global carbon cycle, but they also support a large number of local economies. Intense forest clearing and draining in these peatlands is causing severe ecological and environmental impacts. Most studies highlighted increased carbon emission in the region through drought and large-scale fires, further accelerating peatland degradation. Yet, little is known about the long-term impacts of human-induced disturbance on peatland hydrology in the tropics. Here we show that converting natural peat forests to plantations can significantly alter the hydrological system far worse than previously recognized, leading to amplified moisture stress and drought severity. This study quantified how human-induced changes to Indonesian peatlands have affected drought severity. Through field observations and modelling, we demonstrate that canalization doubled drought severity; logging and starting plantations even quadrupled drought severity. Recognizing the importance of peatlands to Indonesia, proper management, and rehabilitating peatlands remain the only viable option for continued plantation use

Use of near infrared reflectance spectroscopy to predict nitrogen uptake by winter wheat within fields with high variability in organic matter

In this study, the ability to predict N-uptake in winter wheat crops using NIR-spectroscopy on soil samples was evaluated. Soil samples were taken in unfertilized plots in one winter wheat field during three years (1997-1999) and in another winter wheat field nearby in one year (2000). Soil samples were analyzed for organic C content and their NIR-spectra. N-uptake was measured as total N-content in aboveground plant materials at harvest. Models calibrated to predict N-uptake were internally cross validated and validated across years and across fields. Cross-validated calibrations predicted N-uptake with an average error of 12.1 to 15.4 kg N ha-1. The standard deviation divided by this error (RPD) ranged between 1.9 and 2.5. In comparison, the corresponding calibrations based on organic C alone had an error from 11.7 to 28.2 kg N ha-1 and RPDs from 1.3 to 2.5. In three of four annual calibrations within a field, the NIR-based calibrations worked better than the organic C based calibrations. The prediction of N-uptake across years, but within a field, worked slightly better with an organic C based calibration than with a NIR based one, RPD = 1.9 and 1.7 respectively. Across fields, the corresponding difference was large in favour of the NIR-calibration, RPD = 2.5 for the NIR-calibration and 1.5 for the organic C calibration. It was concluded that NIR-spectroscopy integrates information about organic C with other relevant soil components and therefore has a good potential to predict complex functions of soils such as N-mineralization. A relatively good agreement of spectral relationships to parameters related to the N-mineralization of datasets across the world suggests that more general models can be calibrated

Mapping soil organic carbon fractions for Australia, their stocks, and uncertainty

Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuous set of compounds with different chemical compositions, origins, and susceptibilities to decomposition that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia's soils. The three SOC fractions, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric-log-ratio (ilr) transformation to account for the closed compositional nature of SOC fractions. The resulting back-transformed ilr components were mapped across Australia. SOC fraction stocks for 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments, and soil thickness. Mapping was done by a quantile regression forest fitted with the ilr-transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17 %, whereas 28 % ± 17 % was PyOC and 13 % ± 11 % was POC. The allocation of total organic carbon (TOC) to the MAOC fractions increased with depth. SOC vulnerability (i.e. POC/[MAOC+PyOC]) was greater in areas with Mediterranean and temperate climates. TOC and the distribution among fractions were the most influential variables in SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates' importance metric. We estimated the total SOC stocks (0–30 cm) to be 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as a response to land use change, management, and climate change.</p

Using homosoils to enrich sparse soil data infrastructure: an example from Mali

Many areas in the world suffer from relatively sparse soil data availability. This results in inefficient implementation of soil-related studies and inadequate recommendations for improving soil management strategies. Commonly, this problem is tackled by collecting new soil data which are used to update legacy soil surveys. New soil data collection, however, is usually costly. In this paper, we demonstrate how to find homosoils with the objective of obtaining new soil data for a study area. Homosoils are soils that can be geographically distant but share similar soil-forming factors. We cluster the study area into five areas, and identify a homosoil to each area using distance metrics calculated in the character space spanned by the environmental covariates. In a case study in Mali, we found that large areas in India, Australia and America have similar soil-forming factors to the African Sahelian zone. We collected available soil data for these areas from the WoSIS database. Statistical analysis on the relationship between the homosoils corresponding to different areas of Mali and tree soil properties (clay, sand, pH) displayed the unique variability captured by homosoils. The homosoils could explain 8% of the variation found in the soil datasets. There was a strong association between pH and homosoils corresponding to the semi-arid conditions and sedimentary parent material of Mali, whereas homosoils corresponding to other areas of Mali showed moderate association either with clay or sand. The location and spread of the group centroids were statistically significantly different between depth-specific homosoils for the three soil properties. The approach developed in this paper shows the opportunity for identifying areas in the world with similar soils to populate areas with relatively low soil data density. The concept of homosoils is promising and we envision future applications such as transfer of soil models and agronomic experimental results between areas

GlobalSoilMap.net: a new digital soil map of the world.

Knowledge of the world soil resources is fragmented and dated. There is a need for accurate, up-to-date and spatially referenced soil information as frequently expressed by the modelling community, farmers and land users, and policy and decision makers. This need coincides with an enormous leap in technologies that allow for accurately collecting and predicting soil properties. We are working on a new digital soil map of the world using state-of-the-art and emerging technologies for soil mapping and predicting soil properties. Our aim is to map the global land surface in 5 years ? the resulting maps will depict the primary functional soil properties at a grid resolution of 90×90 m. They will be freely available, web-accessible and widely distributed and used.The maps will be produced by a global consortium with centres in each of the continents: NRCS for North America, Embrapa for Latin America, JRC for Europe, TSBF-CIAT for Africa, ISSAS for parts of Asia and CSIRO for Oceania. This new global soil map will be supplemented by interpretation and functionality options that aim to assist better decisions in a range of global issues like food production and hunger eradication, climate change, and environmental degradation. In November 2008, a grant of US$18 million was obtained from the Bill & Melinda Gates Foundation to map most parts of Sub-Saharan Africa and make the underlying data available. From this grant there are funds for coordinating efforts in the global consortium.bitstream/item/66584/1/GlobalSoilMap.net-A-New-Digital-Soil-Map-of-the-World-Guest-Article-MEA-Bulletin-Issue-No.pd