Summary Soil water retention curves are needed to describe the availability of soil water to plants and to model movement of water through unsaturated soils. Measuring these characteristics is time‐consuming, labour‐intensive and therefore expensive. This study was conducted to develop and evaluate functions based on neural networks to predict soil water retention characteristics. Dutch and Scottish data sets were available; they contained data on 178 and 165 soil horizons, respectively. A series of three neural networks (A, B and C) was developed. Neural network A had the following input variables: topsoil, bulk density, organic matter, clay, silt and sand content. In addition neural network B had matric potential as input, and network C included soil structural data expressed as the upper and lower boundary of the ped‐size class. Neural network A had three output variables: the volumetric water content at matric potentials of 0, –100 and –15 000 hPa. Both models B and C had volumetric water content, at the matric potential given as input, as output variable. The networks were tested against independent data that were extracted from the original sets of soil profiles. Accuracy of the predictions was quantified by the root of the mean squared difference (RMSE) between the measured and the predicted water contents, and the coefficient of determination (R2). For network A the RMSE varied for the three estimated water contents from 0.0264 to 0.0476 cm3 cm–3, and R2 varied from 0.80 to 0.93 for the individual model outputs. Networks B and C had an RMSE of 0.0435 and 0.0426 cm3 cm–3, respectively. For both networks, R2 was 0.89. The neural networks performed somewhat better than previous regression functions, but the improvements were not significant.
Precision agriculture (PA) has recently been defined by the U.S. tween multiple factors affecting crop growth and farm National Research Council as a management strategy that uses information technologies to bring data from multiple sources to bear on decision making. Unbiased, systematic and rigorous decisions associated with crop production. Soil information is impor-evaluations of the economic and environmental benefits tant here, but current soil survey data do not satisfy PA requirements. and costs of agriculture are needed. New developments In this paper, the need for soil data in PA is discussed on the basis in precision agriculture need to be evaluated along the of Dutch research. Not only operational, but also tactical and strategic same lines. aspects are considered. On the operational level, soil data, including The advance of ICT is such that we may speak of a parameters derived with pedotransfer functions, support the use of paradigm shift in agricultural research and education simulation models to quantify dynamically soil water regimes, N trans-(National Research Council, 1997). This paper adformations, and biocide adsorption. Real time "forward-looking" simdresses the question as to the implications of this paraulations incorporated in early-warning systems assist in operational digm shift for soil science. Soil science, and more specifidecisions on water, nutrient, and crop protection management. Backward-looking simulations, using historic weather data, can be used to cally pedology, is uniquely positioned to consider the evaluate different management tactics for exploratory strategic and behavior of different soils in a landscape. However, tactical purposes. Such simulations should balance production and the National Research Council (1997) concludes that environmental requirements. At the strategic and tactical level, assem-"current soil surveys satisfy few of the soil data requirebled data on the performance of various farm management systems ments of PA. Soil data are not at the appropriate level should be grouped by soil series to build a systematic database, of detail nor are the indexes required by PA the same allowing "quick and preliminary" evaluations of the effects of farm as those provided by soil surveys". This paper intends management strategies based on experiences obtained elsewhere on to analyse critically this statement and to illustrate the similar soils.
Bypass flow governs solute movement in well‐structured clay soils. Combined use of physical and morphological methods was made in this study to better characterize the process. Flow processes in five 200‐mm‐long undisturbed cores of 200‐mm diameter were monitored at 11‐min intervals by using 102 small transducer tensiometers, divided among five samples and installed at three depths. Flow patterns along macropores were stained with methylene blue. Twenty‐five tensiometers in or within a distance of a few millimeters of stained macropores reacted very quickly when 10 mm of simulated rain was applied at an intensity of 13 mm h−1, and showed a drying pattern after the end of the simulated rainfall. Forty‐three tensiometers inside peds reacted more slowly and showed continued wetting after the end of the simulated rainfall indicating internal catchment of water in the bottom of discontinuous macropores followed by redistribution of water. Internal catchment increased with depth in the samples, as indicated by both physical and morphological data. Using tensiometer measurements as a point count, it is estimated that 33% of the soil volume was in close contact with continuous macropores, while 42% was influenced by the effects of internal catchment. An average of 5.2 ± 0.96 mm of the applied 10 mm of water left the cores through bypass flow, while an estimated average of 3.3 ± 0.96 mm contributed to internal catchment. The observed patterns of water movement illustrate the inadequacy of the concept of mobile/immobile water.
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