Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense dataset

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

Spatial risk assessment of hydrological extremities : Inland excess water hazard, Szabolcs-Szatmár-Bereg Country, Hungary

Inland excess water hazard was regionalized and digitally mapped using auxiliary spatial environmental information for a county in Eastern Hungary. Quantified parameters representing the effect of soil, geology, groundwater, land use and hydrometeorology on the formulation of inland excess water were defined and spatially explicitly derived. The complex role of relief was characterized using multiple derivatives computed from a DEM. Legacy maps displaying inland excess water events were used as a reference dataset. Regression kriging was applied for spatial inference with the correlation between environmental factors and inundation determined using multiple linear regressions. A stochastic factor derived through kriging the residual was added to the regression results,thus producing the final inundation hazard map. This may be of use for numerous landrelated activities

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

Impacts of selected Ecological Focus Area options in European farmed landscapes on climate regulation and pollination services: a systematic map protocol

Background: This systematic map protocol responds to an urgent policy need to evaluate key environmental benefits of new compulsory greening measures in the European Union’s Common Agricultural Policy (CAP), with the aim of building a policy better linked to environmental performance. The systematic map will focus on Ecological Focus Areas (EFAs), in which larger arable farmers must dedicate 5% of their arable land to ecologically beneficial habitats, landscape features and land uses. The European Commission’s Joint Research Centre has used a software tool called the ‘EFA calculator’ to inform the European Commission about environmental benefits of EFA implementation. However, there are gaps in the EFA calculator’s coverage of ecosystem services, especially ‘global climate regulation’, and an opportunity to use systematic mapping methods to enhance its capture of evidence, in advance of forthcoming CAP reforms. We describe a method for assembling a database of relevant, peer-reviewed research conducted in all agricultural landscapes in Europe and neighbouring countries with similar biogeography, addressing the primary question: what are the impacts of selected EFA features in agricultural land on two policy-relevant ecosystem service outcomes—global climate regulation and pollination? The method is streamlined to allow results in good time for the current, time-limited opportunity to influence reforms of the CAP greening measures at European and Member State level. Methods: We will search four bibliographic databases in English, using a predefined and tested search string that focuses on a subset of EFA options and ecosystem service outcomes. The options and outcomes are selected as those with particular policy relevance and traction. Only articles in English will be included. We will screen search results at title, abstract and full text levels, recording the number of studies deemed non-relevant (with reasons at full text). A systematic map database that displays the meta-data (i.e. descriptive summary information about settings and methods) of relevant studies will be produced following full text assessment. The systematic map database will be published as a MS-Excel database. The nature and extent of the evidence base will be discussed, and the applicability of methods to convert the available evidence into EFA calculator scores will be assessed

Soil properties drive microbial community structure in a large scale transect in South Eastern Australia

Soil microbial communities directly affect soil functionality through their roles in the cycling of soil nutrients and carbon storage. Microbial communities vary substantially in space and time, between soil types and under different land management. The mechanisms that control the spatial distributions of soil microbes are largely unknown as we have not been able to adequately upscale a detailed analysis of the microbiome in a few grams of soil to that of a catchment, region or continent. Here we reveal that soil microbes along a 1000 km transect have unique spatial structures that are governed mainly by soil properties. The soil microbial community assessed using Phospholipid Fatty Acids showed a strong gradient along the latitude gradient across New South Wales, Australia. We found that soil properties contributed the most to the microbial distribution, while other environmental factors (e.g., temperature, elevation) showed lesser impact. Agricultural activities reduced the variation of the microbial communities, however, its influence was local and much less than the overall influence of soil properties. The ability to predict the soil and environmental factors that control microbial distribution will allow us to predict how future soil and environmental change will affect the spatial distribution of microbes

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

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

Microbial decomposition of organic matter and wetting–drying promotes aggregation in artificial soil but porosity increases only in wet-dry condition

Aggregation is one of the key properties influencing the function of soils, including the soil’s potential to stabilise organic carbon and create habitats for micro-organisms. The mechanisms by which organic matter influences aggregation and alters the pore geometry remain largely unknown. We hypothesised that rapid microbial processing of organic matter and wetting and drying of soil promotes aggregation and changes in pore geometry. Using microcosms of silicate clays and sand with either rapidly decomposable glucose or slowly decomposable cellulose, the degree of aggregation (P < 0.001), was greater in glucose treatments than controls that did not receive added carbon or microbial inoculum. We link this to microbial activity through measurements in soil respiration, phospholipids and microbially derived carbon. Our results demonstrate that rapid microbial decomposition of organic matter and microbially derived carbon promote aggregation and the aggregation process was particularly strong in the wet-dry condition (alternating between 30 % and 15 % water content) with significant modification of porosity (P < 0.05) of the aggregates