Soil water content (θ) measurement is vital for accurate irrigation scheduling. Electromagnetic induction surveys can be used to map spatial variability of θ when other soil properties are uniform. However, depth-specific θ variations, essential for precision irrigation management, have been less investigated using this method. A quasi-2-dimensional inversion model, capable of inverting apparent soil electrical conductivity (ECa) data to calculate estimates of true electrical conductivity (σ) down the entire soil profile, was developed using ECa data collected by a multi-coil Dualem-421S sensor. The optimal relationships between σ and volumetric water content (θv) were established using all coil arrays of the Dualem-421S, a damping factor of 0.04, an initial model of 35 mSm?1, and with ten iterations (R2?=?0.70, bias?=?0.00 cm3cm?3, RMSE?=?0.04 cm3cm?3). These relationships were then used to derive soil profile images of these properties, and as expected, θv and σ follow similar trends down the soil profile. The derived soil profile images for θv have potential use for irrigation scheduling to two ECa-derived soil management zones under a variable rate irrigation system at this case study site. They reflect the intrinsic soil differences that occur between texture, texture transitions and drainage characteristics. The method can also be used to guide placement of soil moisture sensors for in-season monitoring of spatio-temporal variations of θv. This soil imaging method showed good potential for predicting 2D depth profiles of soil texture, moisture and drainage characteristics, and supporting soil, plant and irrigation management.
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Pierre Roudier Bruno Tisseyre Hervé Poilvé Jean-Michel Roger 《Precision Agriculture》2011,12(1):130-145
Ten years after the introduction of zone-based management to take into account within-field phenomena in agronomic practices, several methodological developments have progressed to the operational level. However, this raises a new scientific question: how can the relevance of this type of management be evaluated? This paper adapts the concept of a technical opportunity index to zone-specific management. Based on the characteristics of machinery, zoning opportunity is introduced through a new index (ZOI) adapted specifically to zone-based management. This index takes into account the operational conditions in which zoning is applied, together with its associated risks. The results obtained on simulated and real field data highlight the relevance of this index. 相似文献
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El-Naggar A. G. Hedley C. B. Roudier P. Horne D. Clothier B. E. 《Precision Agriculture》2021,22(4):1045-1066
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Pierre Roudier Bruno Tisseyre Hervé Poilvé Jean-Michel Roger 《Precision Agriculture》2008,9(5):233-250
Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific
management zones (SSMZ) according to one or several soil or crop characteristics. This paper proposes an original methodology
for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool:
the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The
algorithm was tested on high-resolution bio-physical images of a set of fields in France. 相似文献
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This study investigated the potential for visible–near‐infrared (vis–NIR) spectroscopy to predict locally volumetric soil organic carbon (SOC) from spectra recorded from field‐moist soil cores. One hundred cores were collected from a 71‐ha arable field. The vis–NIR spectra were collected every centimetre along the side of the cores to a depth of 0.3 m. Cores were then divided into 0.1‐m increments for laboratory analysis. Reference SOC measurements were used to calibrate three partial least‐squares regression (PLSR) models for bulk density (ρb), gravimetric SOC (SOCg) and volumetric SOC (SOCv). Accurate predictions were obtained from averages of spectra from those 0.1‐m increments for SOCg (ratio of performance to inter‐quartile (RPIQ) = 5.15; root mean square error (RMSE) = 0.38%) and SOCv (RPIQ = 5.25; RMSE = 4.33 kg m?3). The PLSR model for ρb performed least well, but still produced accurate results (RPIQ = 3.76; RMSE = 0.11 Mg m?3). Predictions for ρb and SOCg were combined to compare indirect and direct predictions of SOCv. No statistical difference in accuracy between these approaches was detected, suggesting that the direct prediction of SOCv is possible. The PLSR models calibrated on the 10‐cm depth intervals were also applied to the spectra originally recorded on a 1‐cm depth increment. While a bigger bias was observed for 1‐cm than for 10‐cm predictions (1.13 and 0.19 kg m?3, respectively), the two populations of estimates were not distinguishable statistically. The study showed the potential for using vis–NIR spectroscopy on field‐moist soil cores to predict SOC at high depth resolutions (1 cm) with locally derived calibrations. 相似文献
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Carolyn Hedley Pierre Roudier Lionel Maddi 《Communications in Soil Science and Plant Analysis》2015,46(4):104-121
The advent of affordable, ground-based, global positioning information (GPS)–enabled sensor technologies provides a new method to rapidly acquire georeferenced soil datasets in situ for high-resolution soil attribute mapping. Our research deployed vehicle-mounted electromagnetic sensor survey equipment to map and quantify soil variability (?50 ha per day) using apparent electrical conductivity as an indirect measure of soil texture and moisture differences. A portable visible–near infrared (VNIR) spectrometer (350–2500 nm) was then used in the field to acquire hyperspectral data from the side of soil cores to a specified depth at optimized sampling locations. The sampling locations were derived by statistical analysis of the electromagnetic survey dataset, to proportionally sample the full range of spatial variability. The VNIR spectra were used to predict soil organic carbon (prediction model using field-moist spectra: R2 = 0.39; RPD = 1.28; and air-dry spectra: R2 = 0.80; RPD = 2.25). These point values were combined with the electromagnetic survey data to produce a soil organic carbon map, using a random forest data mining approach (validation model: R2 = 0.52; RMSE = 3.21 Mg C/ha to 30 cm soil depth; prediction model: R2 = 0.92; RMSE = 1.53 Mg C/ha to 30 cm soil depth). This spatial modeling method, using high-resolution sensor data, enables prediction of soil carbon stocks, and their spatial variability, at a resolution previously impractical using a solely laboratory-based approach. 相似文献
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