Soil ecosystem services (ES) provide multiple benefits to human well-being, but the failure to appreciate them has led to soil degradation issues across the globe. Despite an increasing interest in the threats to soil resources, economic valuation in this context is limited. Importantly, most of the existing valuation studies do not account for the spatial distribution of benefits that soil ES provide to the population. In this study, we present the results of a choice experiment (CE) aimed at investigating spatial heterogeneity of attitudes and preferences towards soil conservation and soil ES. We explored spatial heterogeneity of both attitudes and welfare measures via GIS techniques. We found that citizens of the Veneto Region (Northeast Italy) generally have positive attitudes towards soil conservation. We also find positive willingness-to-pay (WTP) values for soil ES in most of the study area and a considerable degree of heterogeneity in the spatial taste distribution. Finally, our results suggest that respondents with pro-environmental attitudes display a higher WTP based on the geographic pattern of the distribution of WTP values and attitudinal scores across the area.
Abstract Apparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle‐mounted proximal sensors. Crop‐yield data, which is measured by harvester‐mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop‐yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop‐yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop‐yield data. This is achieved through the use of a local regression kriging approach which uses the higher‐resolution crop‐yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop‐management decisions and building more robust calibrations between field‐gathered soil ECa and primary soil properties such as clay content.
The spatial or temporal variability of soil has been extensively considered in the literature using either experimental or modeling approaches. However, only a few studies integrate both spatial and temporal dimensions. The aim of this paper is to present a method for field‐scale simulations of the spatio‐temporal evolution of topsoil organic C (OC) at the landscape scale over a few decades and under different management strategies. A virtual landscape with characteristics matching part of Brittany (France) was considered for the study. Stochastic simulations and regression analysis were used to simulate spatial fields with known spatial structures: short‐range, medium‐range, and long‐range variability. These were then combined using an additive model of regionalization. Agricultural land use was simulated considering four different land uses: permanent pasture, temporary pasture, annual cereal crops, and maize ( Zea mays L.). Land use evolution over time was simulated using transition matrices. Evolution of soil organic matter was estimated each year for each pixel through a rudimentary balance model that accounts for land use and the influence of soil waterlogging on mineralization rates. This spatio‐temporal simulation approach at the landscape level allowed the simulation of several scales of soil variability including within‐field variability. Spatial variability decreased drastically over time when only the influence of land use was considered. This effect on soil variability over the landscape may have implications for site‐specific soil management and precision agriculture. The presence of redoximorphic conditions was found to maintain soil spatial variability.
Abstract. Erosion and excessive runoff from a crusting and hard‐setting red‐brown earth may he ameliorated with suitable management. A field trial, near Cowra, New South Wales, to assess the long‐term effect of different tillage systems was used to compare the effect of direct drilling with conventional district cultivation practices under continuous wheat. The soil was sampled in the eighth year for assessment of the soil macropore structure, measurement of bulk density and hydraulic conductivity under tension. Vertical faces were prepared from resin impregnated blocks and the macropore structure described mathematically and visually using digital images and data generated from these images. Infiltration, bulk density and image analysis data all lead to the same conclusions about changes in pore structure. Under direct drilling no crust was evident, and there was greater macroporosity (> 0.175 mm diameter in section). The treatment effects appeared to be significant to about 30 to 35 mm depth at the time of sampling. Greater root and faunal activity were observed under direct drilling.
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