Site-specific seeding using multi-sensor and data fusion techniques : a review

Site-specific seeding (SSS) is a precision agricultural (PA) practice aiming at optimizing seeding rate and depth, depending on the within field variability in soil fertility and yield potential. Unlike other site-specific applications, SSS was not adopted sufficiently by farmers due to some technological and practical challenges that need to be overcome. Success of site-specific application strongly depends on the accuracy of measurement of key parameters in the system, modeling and delineation of management zone maps, accurate recommendations and finally the right choice of variable rate (VR) technologies and their integrations. The current study reviews available principles and technologies for both map-based and senor-based SSS. It covers the background of crop and soil quality indicators (SQI), various soil and crop sensor technologies and recommendation approaches of map-based and sensor-based SSS applications. It also discusses the potential of socio-economic benefits of SSS against uniform seeding. The current review proposes prospective future technology synthesis for implementation of SSS in practice. A multi-sensor data fusion system, integrating proper sensor combinations, is suggested as an essential approach for putting SSS into practice

A multi sensor data fusion approach for creating variable depth tillage zones

Efficiency of tillage depends largely on the nature of the field, soil type, spatial distribution of soil properties, and the correct setting of the tillage implement. However, current tillage practice is often implemented without full understanding of machine design and capability leading to lowered efficiency and further potential damage to the soil structure. By modifying the physical properties of soil only where the tillage is needed for optimum crop growth, variable depth tillage (VDT) has been shown to reduce costs, labour, fuel consumption and energy requirements. To implement VDT it is necessary to determine and map soil physical properties, spatially and with depth through the soil profile. Up until now the measurement of soil compaction for VDT has been soil penetration resistance, expressed as Cone Index (CI). In this research a multi-sensor and data fusion approach was developed that allowed augmenting data collected with an electromagnetic sensor, a standard penetrometer, and conventional methods for the measurement of bulk density (BD) and moisture content (MC). Packing density values were recorded for eight soil layers of 0-5, 5-10, 10-15, 15-20, 20-25, 25-30 30-35 and 35-40 cm. From the results only 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful tool to guide VDT operations. The results provided in this study indicate that the new multi0sensor and data fusion approach introduced is a useful approach to map layered soil compaction to guide VDT operations. The economic benefit analysis demonstrated fuel savings of 48% by implementing the proposed system. Further work is needed to implement the packing density map for VDT in larger numbers of field in order to generalise the approach

Indicators of soil quality - Physical properties (SP1611). Final report to Defra

The condition of soil determines its ability to carry out diverse and essential functions that support human health and wellbeing. These functions (or ecosystem goods and services) include producing food, storing water, carbon and nutrients, protecting our buried cultural heritage and providing a habitat for flora and fauna. Therefore, it is important to know the condition or quality of soil and how this changes over space and time in response to natural factors (such as changing weather patterns) or to land management practices. Meaningful soil quality indicators (SQIs), based on physical, biological or chemical soil properties are needed for the successful implementation of a soil monitoring programme in England and Wales. Soil monitoring can provide decision makers with important data to target, implement and evaluate policies aimed at safeguarding UK soil resources. Indeed, the absence of agreed and well-defined SQIs is likely to be a barrier to the development of soil protection policy and its subsequent implementation. This project assessed whether physical soil properties can be used to indicate the quality of soil in terms of its capacity to deliver ecosystem goods and services. The 22 direct (e.g. bulk density) and 4 indirect (e.g. catchment hydrograph) physical SQIs defined by Loveland and Thompson (2002) and subsequently evaluated by Merrington et al. (2006), were re-visited in the light of new scientific evidence, recent policy drivers and developments in sampling techniques and monitoring methodologies (Work Package 1). The culmination of these efforts resulted in 38 direct and 4 indirect soil physical properties being identified as potential SQIs. Based on the gathered evidence, a ‘logical sieve’ was used to assess the relative strengths, weaknesses and suitability of each potential physical SQI for national scale soil monitoring. Each soil physical property was scored in terms of: soil function – does the candidate SQI reflect all soil function(s)? land use - does the candidate SQI apply to all land uses found nationally? soil degradation - can the candidate SQI express soil degradation processes? does the candidate SQI meet the challenge criteria used by Merrington et al. (2006)?This approach enabled a consistent synthesis of available information and the semi-objective, semi-quantitative and transparent assessment of indicators against a series of scientific and technical criteria (Ritz et al., 2009; Black et al., 2008). The logical sieve was shown to be a flexible decision-support tool to assist a range of stakeholders with different agenda in formulating a prioritised list of potential physical SQIs. This was explored further by members of the soil science and soils policy community at a project workshop. By emphasising the current key policy-related soil functions (i.e. provisioning and regulating), the logical sieve was used to generate scores which were then ranked to identify the most qualified SQIs. The process selected 18 candidate physical SQIs. This list was further filtered to move from the ‘narrative’ to a more ‘numerical’ approach, in order to test the robustness of the candidate SQIs through statistical analysis and modelling (Work Package 2). The remaining 7 physical SQIs were: depth of soil; soil water retention characteristics; packing density; visual soil assessment / evaluation; rate of erosion; sealing; and aggregate stability. For these SQIs to be included in a robust national soil monitoring programme, we investigated the uncertainty in their measurement; the spatial and temporal variability in the indicator as given by observed distributions; and the expected rate of change in the indicator. Whilst a baseline is needed (i.e. the current state of soil), it is the rate of change in soil properties and the implications of that change in terms of soil processes and functioning that are key to effective soil monitoring. Where empirical evidence was available, power analysis was used to understand the variability of indicators as given by the observed distributions. This process determines the ability to detect a particular change in the SQI at a particular confidence level, given the ‘noise’ or variability in the data (i.e. a particular power to detect a change of ‘X’ at a confidence level of ‘Y%’ would require ‘N’ samples). However, the evidence base for analysing the candidate SQIs is poor: data are limited in spatial and temporal extent for England and Wales, in terms of a) the degree (magnitude) of change in the SQI which significantly affects soil processes and functions (i.e. ‘meaningful change’), and b) the change in the SQI that is detectable (i.e. what sample size is needed to detect the meaningful signal from the variability or noise in the signal). This constrains the design and implementation of a scientifically and statistically rigorous and reliable soil monitoring programme. Evidence that is available suggests that what constitutes meaningful change will depend on soil type, current soil state, land use and the soil function under consideration. However, when we tested this by analysing detectable changes in packing density and soil depth (because data were available for these SQIs) over different land covers and soil types, no relationships were found. Schipper and Sparling (2000) identify the challenge: “a standardised methodology may not be appropriate to apply across contrasting soils and land uses. However, it is not practical to optimise sampling and analytical techniques for each soil and land use for extensive sampling on a national scale”. Despite the paucity in data, all seven SQIs have direct relevance to current and likely future soil and environmental policy, because they can be related (qualitatively) to soil processes, soil functions and delivery of ecosystem goods and services. Even so, meaningful and detectable changes in physical SQIs may be out of time with any soil policy change and it is not usually possible to link particular changes in SQIs to particular policy activities. This presents challenges in ascertaining trends that can feed into policy development or be used to gauge the effectiveness of soil protection policies (Work Package 3). Of the seven candidate physical SQIs identified, soil depth and surface sealing are regarded by many as indicators of soil quantity rather than quality. Visual soil evaluation is currently not suited to soil monitoring in the strictest sense, as its semi-qualitative basis cannot be analysed statistically. Also, few data exist on how visual evaluation scores relate to soil functions. However, some studies have begun to investigate how VSE might be moved to a more quantified scale and the method has some potential as a low cost field technique to assess soil condition. Packing density requires data on bulk density and clay content, both of which are highly variable, so compounding the error term associated with this physical SQI. More evidence is needed to show how ‘meaningful’ change in aggregate stability affects soil processes and thus soil functions (for example, using the limited data available, an equivocal relationship was found with water regulation / runoff generation). The analysis of available data has given promising results regarding the prediction of soil water retention characteristics and packing density from relatively easy to measure soil properties (bulk density, texture and organic C) using pedotransfer functions. Expanding the evidence base is possible with the development of rapid, cost-effective techniques such as NIR sensors to measure soil properties. Defra project SP1303 (Brazier et al., 2012) used power analyses to estimate the number of monitoring locations required to detect a statistically significant change in soil erosion rate on cultivated land. However, what constitutes a meaningful change in erosion rates still requires data on the impacts of erosion on soil functions. Priority cannot be given amongst the seven SQIs, because the evidence base for each varies in its robustness and extent. Lack of data (including uncertainty in measurement and variability in observed distributions) applies to individual SQIs; attempts at integrating more than one SQI (including physical, biological and chemical SQIs) to improve associations between soil properties and processes / functions are only likely to propagate errors. Whether existing monitoring programmes can be adapted to incorporate additional measurement of physical SQIs was explored. We considered options where one or more of the candidate physical SQIs might be implemented into soil monitoring programmes (e.g. as a new national monitoring scheme; as part of the Countryside Survey; and as part of the National Soil Inventory). The challenge is to decide whether carrying out soil monitoring that is not statistically robust is still valuable in answering questions regarding current and future soil quality. The relationship between physical (and other) SQIs, soil processes and soil functions is complex, as is how this influences ecosystem services’ delivery. Important gaps remain in even the realisation of a conceptual model for these inter-relationships, let alone their quantification. There is also a question of whether individual quantitative SQIs can be related to ecosystem services, given the number of variables

Visible and near infrared spectroscopy in soil science

This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction

Development and Assessment of Nematode Management Zones in Cotton: \u3ci\u3eGossypium hirsutum\u3c/i\u3e

Populations of plant-parasitic nematodes are difficult to manage due to their inherently sporadic nature and uneven distribution throughout a field. Soil sampling accompanied by laboratory extraction is the preferred method for estimating densities and locations of nematodes within a field. The uneven and sporadic nature of nematodes make them well suited for zone management in row crops, provided that effective zones can be defined. Effective zone definition for precision agriculture requires that differences in factors between zones are large and differences within zones are small. This study compared methods of defining zones based on physical soil properties, soil SSURGO data, and grids of similar area to cost-effectively direct nematode sampling efforts. Twenty-six methods of zone definition were investigated based on soil electrical conductivity (EC), physical soil properties and relative nematode index predictions in various combinations. For each zone definition method, the fitness of models used to define zones was evaluated using the Davies-Bouldin Index (DBI) for measuring cluster separation where effectiveness of zone definitions decrease as the DBI increases. The DBI range for all zone methods investigated was 24.918, with a minimum of 5.086 and maximum of 30.004. The most effective zone was created by contouring relative weighted nematode index predictions, with predictions based on soil EC, with a delineation range of one standard deviation, which returned the lowest DBI. Zones created based on a three equal range division of field silt levels returned the highest DBI indicating the least effective zone method. Using silt content in any range delineation showed to be inappropriate for zone definition. The two highest DBI values returned were when silt was used at a range delineation of 0.5 standard deviation, DBI of 29.0399, and a three equal division range, DBI of 30.004. Use of SSURGO soil data was also found to be significantly less effective for defining zones with a DBI of 27.155 compared with zones definitions based on soil EC. Zones defined using soil EC as a contributing factor demonstrated significantly effective zones. Of the nine zone definitions that were significantly effective, seven were defined using soil EC as some factor. A second goal of this project was to asses multi-hybrid planting technology as a tool for the management of nematodes. Cotton varieties are now available that are resistant to Southern root-knot nematode, the most common and important species on cotton. For this study, a field was chosen based on the ability to grow two consecutive years of cotton within a two-year cotton to one-year peanut crop rotation and an unknown distribution of nematode density and species. This field did not return Southern root-knot nematode densities in adequate quantities for any solid conclusions to be made as to the use of resistant cotton varieties for determination of Southern root-knot nematode aggregations to be used as a basis for multi-hybrid planting or variable rate application for nematode control. The cost of this approach can be prohibitive as it can include higher seed costs, planter upgrades, and creation of planting prescriptions, which may be based on costly nematode sampling. If accurate nematode sampling zones can be determined, the overall cost of implementing this technology can be reduced

Utilizing commercial soil sensing technology for agronomic decisions

Planters with mounted proximal soil sensing systems can densely quantify seed zone soil variability. Technology now allows for real-time sensor information to control multiple row-unit functions on-the-go (e.g., planting depth). These and other developing sensor-based control systems have the potential to greatly improve correctness when planting, and therefore row-crop performance. For sensor-based control to be widely adopted, practitioners must understand the precision and utility of the systems. Therefore, research was conducted to: (i) determine how well commercially available sensors can estimate soil organic matter (OM) and whether sensor output was repeatable among sensing dates; (ii) evaluate OM prediction accuracy across selected soils and soil volumetric water contents with both a commercially-available, planter-mounted sensor, and machine learning techniques applied to multiple combinations of soil reflectance bands within the visible and near infrared spectrum; and (iii) investigate if planter and other proximal soil sensor data, in combination with topographic features, could predict field-scale corn emergence rate at varying planting depths. Results found that commercial sensors could estimate general trends in spatial variability of OM, but that some inconsistencies were associated with a "global" calibration that appeared susceptible to temporal variations in soil water content. In the controlled environment, results for sensor estimation of OM were similar to the field study. Further, results showed that spectral information within the entire range used by the commercial systems evaluated was required to consistently predict OM at varying volumetric water contents. Lastly, the field-scale agronomic analysis found that inherent soil and landscape variability drove the emergence rate response at the site. However, planter metrics were still usefulIncludes bibliographical references

A novel heat-pulse probe for measuring soil thermal conductivity: Field test under different tillage practices

Accurate soil thermal conductivity (λ) measurements are needed in experimental agri-environmental research. This study design and build of new heat-pulse probe (HPP) based on transient state technology to measure λ. The HPP consists of three main components: an electronic control system, a measurement chamber, and sample rings. The performance of the new HPP for in-situ λ measurements is compared to estimates from measurable soil physical properties (pedotransfer function). Tests were conducted in clay loam and loam soils at three depths. λ measurements by the HPP were affected by tillage practice, fertilizer treatment, soil depth, and soil type. No significant differences in λ measurements by the HPP and estimates from a pedotransfer function were found between tillage practices. There were positive correlations between their values at three soil depths: R2 = 0.92 at 0–5 cm depth, and R2 = 0.88 at both 5–10 and 10–15 cm depths. The standard deviation from the HPP measurements were 0.061, 0.077, and 0.080 W·m−1·K−1 at 0–5, 5–10, and 10–15 cm depths, respectively. In contrast, the pedotransfer function estimates had standard deviations of 0.085, 0.660, and 0.083 W·m−1·K−1, respectively. It was found that conventional tillage increases temperature flow in soils compared to no-tillage because of decreasing soil bulk density (ρb) and consequently higher porosity. The proposed HPP is a low-cost and energy-efficient device, with wide applicability under a range of conditions. It is highly recommended for measuring λ clay loam and loam soils; however, more research is needed to determine its value with other soil types

Sensors Application in Agriculture

Novel technologies are playing an important role in the development of crop and livestock farming and have the potential to be the key drivers of sustainable intensification of agricultural systems. In particular, new sensors are now available with reduced dimensions, reduced costs, and increased performances, which can be implemented and integrated in production systems, providing more data and eventually an increase in information. It is of great importance to support the digital transformation, precision agriculture, and smart farming, and to eventually allow a revolution in the way food is produced. In order to exploit these results, authoritative studies from the research world are still needed to support the development and implementation of new solutions and best practices. This Special Issue is aimed at bringing together recent developments related to novel sensors and their proved or potential applications in agriculture

Macro- and microscale gaseous diffusion in a Stagnic Luvisol as affected by compaction and reduced tillage

Intensification of mechanical agriculture has increased the risk for soil compaction and deformation. Simultaneously, reduced tillage practices have become popular due to energy saving and environmental concerns, as they may strengthen and improve the functioning of structured soil pore system. Soil aeration is affected by both compaction and reduced tillage through changes in soil structure and in the distribution of easily decomposable organic matter. We investigated whether a single wheeling by a 35 000 kg sugar-beet harvester in a Stagnic Luvisol derived from loess near Göttingen, Germany, influenced the gas transport properties (air permeability, gaseous macro- and microdiffusivities, oxygen diffusion rate) in the topsoil and subsoil samples, and whether the effects were different between long-term reduced tillage and mouldboard ploughing. Poor structure in the topsoil resulted in slow macro- and microscale gas transport at moisture contents near field capacity. The macrodiffusivities in the topsoil under conventional tillage were slower compared with those under conservation treatment, and soil compaction reduced the diffusivities by about half at the soil depths studied. This shows that even one pass with heavy machinery near field capacity impairs soil structure deep into the profile, and supports the view that reduced tillage improves soil structure and aeration compared with ploughing, especially in the topsoil