Soil Sensing

Adamchuk, Viacheslav I.; Biswas, Asim; Huang, Hsin-Hui; Holland, Jonathan; Taylor, James A.; Stenberg, Bo; Wetterlind, Johanna; Singh, Kanika; Minasny, Budiman; Fidelis, Chris; Yinil, David; Sanderson, Todd; Snoeck, Didier; Field, Damien J.

doi:10.1007/978-3-030-78431-7_4

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…Indeed, these results suggest that soil moisture patterns should be reassessed every few weeks at least. Also, as variable irrigation rates are applied to different zones, the management effect will end up changing spatial patterns in relation to initial patterns as Adamchuk et al [63], Kerry et al [66] and Kerry et al [64] have shown. The accuracy of the 8 April drone data predictions is likely due to the fact that the minimum and maximum values were very close to the mean as there was so little variation in soil moisture in this pre-irrigation season, with very dry sampling time.…”

Section: Rmses For Drone Datamentioning

confidence: 99%

“…Such approaches capitalize on the density of the sensed data and the correct range of values of the sample data. In reviewing approaches for mapping soil properties using sensed information in precision agriculture, Adamchuk et al [63] noted that usually, some ground truth data on the properties themselves need to be collected to calibrate the sensed data. Traditional field sampling and lab analysis is expensive, and the cost is proportional to the sample size [64]; therefore, it is desirable to use the smallest sample size that gives the best estimates.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the time taken to conduct grid surveys of turfgrass health and soil properties varies with sample size [29,30]. The sample sizes discussed in the case studies presented by Adamchuk et al [63] were for mapping soil nutrients, physical properties and organic matter levels in agricultural fields, in conjunction with sensed data, but ranged from 10 to 75 samples and were usually based on a regression approach for calibration. Kerry et al [65] investigated the use of an approach calibrating z-scores of sensed data with the means and standard deviations of samples of different sizes .…”

Section: Introductionmentioning

confidence: 99%

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Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Kerry,

Ingram,

Sanders

et al. 2024

Agronomy

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Turfgrass irrigation consumes a large amount of the scarce freshwater in arid/semi-arid regions. Approximately 50% of this irrigation water is wasted. It has been suggested that determining patterns of spatial variability in soil moisture to modify applications with valve-in-head sprinkler technology can greatly reduce waste. Variable rate irrigation (VRI) studies in traditional agricultural settings have shown that VRI zones do not stay static temporally and need to be frequently redetermined. Electrical conductivity (ECa) data from Geonics EM38 surveys and data from Red, Green, Blue (RGB) and Thermal Infra-Red (Th.IR) drone surveys are less time-consuming and therefore expensive to collect than a dense field survey of soil moisture and grass health to produce accurate geostatistical maps. Drone flights and ECa surveys are compared here for their ability to accurately estimate spatial patterns of soil volumetric water content (VWC) using simple linear regression and z-score transformations for prediction—non-geostatistical approaches that require less data. Overall, ECa readings collected in the horizontal mode were the most consistent at capturing spatial patterns in soil moisture. Predictions from regression produced lower root mean squared errors (RMSEs) for the larger datasets. However, z-score transformation produced lower RMSEs when the sample number was very small and preserved the scale of values better than the regression approach. The results suggested that predictions from ECa and drone data were useful for capturing key features in soil moisture patterns for 2–3 weeks, suggesting that a periodic reassessment of zones is needed. Using ECa and drone data in an urban environment is more labor-intensive than in an agricultural field, so it is likely that automating periodic re-surveying of ECa data for zone definition would only be cost-effective for golf courses or high-income sports fields. Elsewhere, using static zones with variable rates applied to each zone may be more efficient.

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Section: Rmses For Drone Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Kerry,

Ingram,

Sanders

et al. 2024

Agronomy

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Remote Sensing Data to Support Integrated Decision Making in Cultural and Natural Heritage Management. Impasses and opportunities for collaboration in agricultural areas

Opitz¹,

Baldwin²,

Smedt³

et al. 2023

Internet Archaeol.

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Remote and near surface sensing data are widely used in archaeology and heritage management for feature discovery, change detection and monitoring, as an input to predictive modelling, and in the planning process. While global and regional datasets are widely used for some purposes, data are regularly acquired specifically for archaeological projects because of the very high spatial resolution required for feature detection and assessments of archaeological significance and the need for data on subsurface features. The sensing data collected for archaeology cover limited areas and only a few types of sensors, known to produce data efficiently, are regularly employed. Precision agriculture is beginning to produce large quantities of varied sensing data across extensive landscape areas. This situation creates an opportunity to adapt and reuse precision agricultural data for archaeology and heritage work, extending covering and enhancing our understanding of archaeology in contemporary agricultural landscapes. Equally, there is potential for coordinated data collection, collecting data once for multiple applications, and to add value through analyses which bring together perspectives from multiple related domains to model long-term processes in anthropogenic soil systems. This article provides a high-level overview of policy and technological developments which create the potential for sensing data reuse, coordinated data collection, and collaborative analyses across archaeological, agricultural, and agri-environmental applications while underscoring the structural barriers which, at present, constrain this potential. It highlights examples where the development of interoperable data and workflows can promote tighter integration of archaeology and cultural heritage management with sustainable agricultural land management and support integrated decision making.

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How to Analyze, Detect and Adjust Variable Seedbed Depth in Site-Specific Sowing Systems: A Case Study

et al. 2022

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Sowing or seedbed depth is an important agro-technological parameter that varies with specific on-field soil and microclimatic conditions and depends on crop biology. There is a lack of detailed information regarding how seedbed depth relates to other seedbed parameters and affects the development of agricultural crops. Several seeder constructions and methods for seeding condition detection and depth adjustment have been investigated in high-precision, digitally backgrounded, in-site sowing systems; however, there is still a gap in knowledge due to the limited use of these technologies in conditions of high soil and micro-climatic variability. Therefore, the aim of this study was to highlight the impact of sowing depth on crop seedbed parameters, mainly established by the Kritz method, to ascertain the correlation between sowing depth, germination, crop development and productivity, and to overview the methods and equipment used for detection, adjustment and control of sowing depth in precision site-specific sowing systems. Our results showed that, in most cases, when sowing depth extended beyond the optimum, the moisture content in the seedbed decreased significantly. Sowing depth also correlated with the roughness of the seedbed (surface and bottom) and seedbed aggregate size distribution, but the direction of the relation depended on crop type and maximum sowing depths. Sowing depth correlated with crop germination, development and productivity parameters; however, the direction of exposure and intensity also varied with respect to crops, weather conditions, tillage and sowing equipment. Sowing depth uniformity is greatly influenced by the regulation of clamping force, the spatial variability of soil in fields and sowing operation speed.

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Soil Sensing

Cited by 6 publications

References 48 publications

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Remote Sensing Data to Support Integrated Decision Making in Cultural and Natural Heritage Management. Impasses and opportunities for collaboration in agricultural areas

How to Analyze, Detect and Adjust Variable Seedbed Depth in Site-Specific Sowing Systems: A Case Study

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