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With a significant growth in the agricultural technology industry, a vast amount of agricultural data is now being collected on farms throughout the world. Farmers aim to utilise these technologies to regularly record and manage the variation of crops and soils within their fields, to reduce inputs, increase yields and enhance environmental sustainability. In this paper, we aim to highlight the variety of different data types and methodological processes involved in modern precision farming systems and explore how potentially interconnected these systems are with the archaeological community. At present, no research has studied the effects of archaeological sites on soils in the context of precision farming practices. Yet from modern geophysical, geochemical and remote sensing techniques, a much greater volume of soil- and crop-related mapping is being undertaken, with huge potential for all kinds of archaeological study. From heritage management to archaeological prospection, how will the future of archaeological studies fit into this changing agricultural landscape?
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ORIGINAL PAPER
Precision farming and archaeology
Henry Webber
1
&Volker Heyd
1
&Mark Horton
1
&Martin Bell
2
&Wendy M at th ew s
2
&
Amanda Chadburn
3
#The Author(s) 2017. This article is an open access publication
Abstract With a significant growth in the agricultural tech-
nology industry, a vast amount of agricultural data is now
being collected on farms throughout the world. Farmers aim
to utilise these technologies to regularly record and manage
the variation of crops and soils within their fields, to reduce
inputs, increase yields and enhance environmental sustainabil-
ity. In this paper, we aim to highlight the variety of different
data types and methodological processes involved in modern
precision farming systems and explore how potentially inter-
connected these systems are with the archaeological commu-
nity. At present, no research has studied the effects of archae-
ological sites on soils in the context of precision farming prac-
tices. Yet from modern geophysical, geochemical and remote
sensing techniques, a much greater volume of soil- and crop-
related mapping is being undertaken, with huge potential for
all kinds of archaeological study. From heritage management
to archaeological prospection, how will the future of archae-
ological studies fit into this changing agricultural landscape?
Keywords Precision farming .Geophysics .Geochemistry .
Remote sensing .Archaeology .Soils
Introduction
During the last decade, significant growth in the agricultural
technology industry has enabled vast amounts of agricultural
data to be collected on farms across the globe. These datasets
are increasingly of a high resolution and cover much larger
spatial and temporal ranges than previous agricultural
datasets. From mapping within-field soil variability to GPS-
based auto-steering, this revolution of twentieth century agri-
culture is changing how farms are run and will continue to in
the future. The question is, how does archaeology fit into this
new agricultural landscape? Many archaeological remains,
known and unknown, lie within agricultural soils and under
the management of standardfarm operations. Many geo-
physical surveys are completed each year to identify and char-
acterise archaeological sites. But what will the future bring for
archaeological prospection and heritage management within
these changing agricultural landscapes?
Precision farming
Precision farmingpractices, also known as precision agri-
cultureor site-specific farming, have been used within ag-
riculture for several decades, but within the past few years
have become more common. Precision farming aims to utilise
technology to record and manage the variation of crops and
soils within a field, thus reducing surplus inputs (e.g.
fertiliser), increasing yields and aiding environmental sustain-
ability (Oliver et al. 2013;Stafford2000). Precision farming
includes many technologies such as satellite imagery, geo-
physics, yield mapping and global positioning systems en-
abling variable rate fertiliser application and variable depth
cultivation, all integrated within farm management software
(JRC and MARS 2014). Precision farming represents a new
*Henry Webber
Henry.Webber@bristol.ac.uk
1
Department of Archaeology and Anthropology, University of Bristol,
43 Woodland Road, Bristol BS8 1UU, UK
2
Department of Archaeology, University of Reading, Whiteknights,
PO Box 218, Reading RG6 6AA, UK
3
Historic England, 29 Queen Square, Bristol BS1 4ND, UK
https://doi.org/10.1007/s12520-017-0564-8
Received: 21 April 2017 /Accepted: 3 November 2017 / Published online: 22 November 2017
Archaeol Anthropol Sci (2019) 11:727734
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
level of high-resolution data collection on farms with regard to
soil mapping, soil nutrient analysis and crop growth data. All
of these can lead to better analytics using data from real farms
for informing management decisions instead of small, repli-
cated trial plots that do not compare to real world applications.
In this sense, precision farming may transform agronomic
science into advice that is targeted to each farms individual
social, economic and environmental context.
There are several motivations for farmers to take up preci-
sion farming methods and the associated initial costs (Zhang
et al. 2002). These can vary, depending on the nature of the
local environment, the policy landscape and economic envi-
ronment (McBratney et al. 2005). In the UK, farmers base
their engagement with precision farming technology on the
practicality of adapting it to existing farm machinery and
farmersperceptions of what methods workand which do
not (Parliamentary Office for Science and Technology
2015).Currently, around 60% of the UKsfarmlandisunder
some form of precision farmingmanagement (http://www.
nesta.org.uk/blog/precision-agriculture-separating-wheat-
chaff accessed on 7 November 2016) (Fig. 1).
Soils: connecting archaeology to precision farming
One of the key underpinning knowledge bases of precision
farming is the variability of soils. Due to the, often, large areas
covered by farmers, this usually is assessed by mapped data,
without actual field inspection. To understand the variation in
a crop remotely, and more importantly what is causing it, one
must understand the variation in the soils at the appropriate
scale. Only then can alternative causes be established by fur-
ther inspection (e.g. pest/disease scouting) and corrected if
necessary. This resolution was previously at the whole-field
level, but is now necessary at the 520 m level to manage
small areas effectively, and is possible due to more accurate
machinery. Archaeologists depend on soils just as much.
Archaeological remains buried within the soil profile depend
on that soil for their conservation. Soils also provide a unique
context for learning about the archaeological remains them-
selves both spatially and vertically, providing vital and some-
times detailed records of soil history and depositional process-
es. To interpret anomalies correctly, archaeological geophysi-
cists regularly require more detailed and higher-resolution in-
formation of soils than is available from existing soil maps.
Geochemical studies of archaeological sites equally need a
robust grounding in the geochemical variation of soils at the
appropriate resolutions to enable accurate interpretations.
Soils, and how those soils vary over space and time, are
clear connections between archaeology and precision farming.
The resolutions of agricultural and archaeological datasets are
far more interconnected and complimentary now, than in the
past. Traditionally in the UK, common agricultural
perspectives consider archaeological sites as generally small
in extent and agriculturally insignificant. These perspectives
can be misleading and do not consider the wide variation of
different types of known archaeological sites that may be of
agricultural significance. Simultaneously, the increasing level
of detail in agricultural management also magnifies the poten-
tial for archaeological sites to have more agricultural impact.
However, from an archaeological perspective, there may be
many archaeologists who are unaware of the types and reso-
lutions of data that now exist in the agricultural world and how
they may relate to archaeological prospection and heritage
management. Here are clear overlaps of not onlydata, but also
the interest in and the desire to understand soils better.
Examples
The following are a few examples taken from case study sites
in the UK to illustrate the ideas discussed above. They are not
meant to present conclusive research that has been fully eval-
uated, but are intended to promote thinking in this new area of
research and suggest areas of future debate.
Soil management zones
A common approach to precision farming in many countries is
the zone management approach (Whelan and McBratney
2003). This approach aims to identify soil variations, map
them and characterise them, to inform better management.
An example of this approach is illustrated in Fig. 2. Here,
freely available satellite imagery, geological mapping from
the British Geological Survey and a soil reflectance image
from the Intelligent Precision Farming company were com-
bined with other types of data (e.g. a farmers own interpreta-
tion) to produce soil management zones. The precision farm-
ing company then samples those zones for soil nutrients
(available phosphate, potash, magnesium and pH) and auto-
matically creates variable rate fertiliser plans for each zone.
The geophysical survey (Fig. 2), for the same site, shows
potentially how an archaeological site might interact in this
situation. The magnetic gradiometer survey shows two linear
anomalies enclosing the centre ground, with an interpreted
small Iron Age enclosure in the smaller field with several
pit-like anomalies. This enclosure does not show clearly in
any of the existing soil data, yet does influence crop growth
in a Normalised Difference Vegetation Index (NDVI) satellite
image from the 27 February 2015 (see Fig. 3). This demon-
strates that the typical precision farming approach to soil map-
ping (although suited to a certain scale) can miss soil variation
that transcends these scales, and that could have been ac-
knowledged if existing archaeological information was in-
cluded initially in the soil zoning process. This omission could
728 Archaeol Anthropol Sci (2019) 11:727734
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also mislead further interpretations as to why this area of crop
was different, or how it could be managed in the future.
Remote sensing
Remotely sensed satellite imagery can be used for a wide
variety of applications within precision farming (Seelan
et al. 2003;Mulla2013). The soil brightness image in Fig. 2
represents light reflectance from the soil surface in four differ-
ent wavebands (including infrared) to identify changes in soil
texture, organic matter, moisture, calcium carbonate and stone
content at a 5-m resolution (http://www.ipf-uk.com/precision-
farming/soil-zoning/soil-brightness.html accessed 27
February 2016). Other spectral characteristics are also used
to determine how healthy a crop is at certain times of the
year. The most common vegetative index used within
precision farming is the NDVI index. This produces results
relating to a cropsgreennessand its leaf area index. Due to
the increasing temporal availability and spatial resolution of
satellite imagery, this technique has seen much use in the UK,
and internationally, to monitor crop health, weeds and even
drainage throughout the growing season (Lamb and Brown
2001;https://sa.catapult.org.uk/documents/10625/53165/
The+Courtyard+Partnership+case+study/26073572-f15f-
41ae-8480-9857b682e84e accessed 27 February 2016).
Figure 3shows three satellite images typical of precision
farming application in the UK. These images are used to plan
nitrogen fertiliser applications variably across the field, for
example to feed poor areas of crop, or reduce application on
nutrient-rich areas. They help to provide farmers with a quick
method of assessing their crops, at the same time as providing
a detailed record of crop growth throughout the year.
Comparing the image from the 27 February 2015 to the
geophysical survey shown in Fig. 2, there are clear correlations
between the small Iron Age enclosure and the growth of oilseed
rape at that time. This is interesting for two reasons; firstly,
oilseed rape is not a crop well known for producing archaeo-
logical crop marks due to its low plant density and branching
canopy. Secondly, during the winter, it is not common to gather
aerial imagery because of poor weather conditions but also due
to the expected lack of archaeological feature detection (i.e.
moisture deficit). Yet perhaps there is progress to be made from
talking to farmers about satellite data, helping them to under-
stand the variations seen from an agricultural, pedological and
an archaeological stance without which, anomalies in satellite
and other data may be wrongly associated. It may also be help-
ful for archaeologists to understand the possibilities that a range
of crops may produce archaeological crop marks under certain
conditions and at times of the phenological cycle not realised
before. In addition to this is the obvious advantage that there is
simply more temporal and spatial data out there which, if it can
be accessed, could be used to enhance the archaeological record
if interpreted correctly.
Soil geophysics
The use of geophysical surveys within precision farming has
mainly focused on electrical conductivity surveys (Allred
Fig. 1 An image of the Toolboxinterface for farmers and agronomists (courtesy of the Intelligent Precision Farming company)
Archaeol Anthropol Sci (2019) 11:727734 729
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730 Archaeol Anthropol Sci (2019) 11:727734
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et al. 2008). As the conductivity of the soil is affected by
several important soil parameters such as moisture, salinity,
texture, stone content and soil depth, it has proven useful to
farmers as a broad indicator of agricultural soil quality. The
typical types of geophysical surveys carried out by precision
farming companies to determine soil management zones are
often at an interval of between 10 and 24 m (see Fig. 4).
At the same time, archaeological geophysicists have devel-
oped a wide range of experiences using multiple near-surface
geophysical methods to learn about the nature of archaeolog-
ical sites. No one method has become the sole tool for an
archaeological geophysicist since flexibility is essential when
surveying a wide range of site types. Magnetic gradiometry is
one of the mostcommonly used methods in the archaeological
geophysics sector in most academic departments, commercial
units and other organisations, but has not yet been subject to
research in an agricultural context despite being noted as a
method of potential (Allred et al. 2008).
An illustration of a conductivity survey is shown in Fig. 4and
is similar to what would be produced by a precision farming
company. In this interesting image, there are clearly some anom-
alies, yet without any more detailed survey, it could not be
interpreted whether these anomalies were of archaeological or-
igin or not. Crucially, however, as a broad survey of the soil
variability, this image has value for future archaeological work
on the site, enabling an initial focus on areas of soil that are most
variable and hold higher potential for archaeological activity.
Soil geochemistry
During the early twentieth century, the Swedish agronomist
Olaf Arrhenius gained extensive experience in phosphate sur-
veys while working for a sugar beet company (Lambert 1998).
He was acclaimed as one of the first people to suggest phos-
phate surveys could be used to prospect for archaeological
sites. Since then, phosphate surveys have fluctuated in their
use within archaeology, but have continued to be essential for
farmers to maintain soil nutrient levels.
Companies and farmers using precision farming methods
take a detailed interest in soil geochemistry. It is important for
them to monitor and adjust macronutrient (N, P, K, Mg) and
micronutrient levels every few years according to what the
crop is expected to need and the nutrients expected to leave
the field with the crop (Heege 2013). Macronutrients are rou-
tinely tested every three to 4 years in the UK at a field level,
Date:
Resoluon:
Crop:
1st of May 2013
5x5m
Winter Wheat
27th of February 2015
10x10m
Oilseed Rape
11th of April 2015
5x5m
Oilseed Rape
Band Colour
No data
-1.0 - 0.1
0.1 - 0. 2
0.2 - 0. 3
0.3 - 0. 4
0.4 - 0. 5
0.5 - 0. 6
0.6 - 0. 7
0.7 - 0. 8
0.8 - 0. 9
0.9 - 1. 0
Fig. 3 NDVI images of the same field under different crops and at different resolutions
Fig. 4 Results of a precision farming conductivity survey, with the data
points used to interpolate the image. Courtesy of James Price
Fig. 2 Data used in a precision farming soil management zoning process
and a magnetic gradiometer survey of the same site with Iron Age
enclosure (images courtesy of Paul Cheetham and Amy Green (Green
2014), Intelligent Precision Farming, Bing Imagery and the British
Geological Society)
Archaeol Anthropol Sci (2019) 11:727734 731
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but today with precision farming, most soil sampling is
targeted to specific soil zones, or a systematic hectare grid,
providing more detailed spatial information about nutrient
variations than before.
Since Arrheniuswork, it has been recognised that archae-
ological sites do not only exhibit variations in phosphate.
Archaeological sites have the potential to enhance or deplete
levels of many stable nutrients due to the siteshistory.
Research has shown that elements such as Ba, Ca, Cu, P, Pb,
Zn and Sr are often found in conjunction with archaeological
activities (Wilson et al. 2008). Little research has, however,
been directed at the implications of these geochemical varia-
tions from an agronomic point of view. As precision farming
represents attention to detail in soils and crops, it seems there
needs to be better understanding of how archaeological sites
contribute to agronomic variation.
Figure 5gives some visual representation of one example
demonstrating this geochemical interconnection. There is an
enhancement of zinc surrounding the area interpreted from
the geophysical survey and confirmed with excavation, to be
a Neolithic henge. This enhancement is twice the average
values of the surrounding soils. The question is, is this en-
hancement due to the complexityof soil variations in this field?
Or is it caused by the human activity involved in creation of the
henge, or is it evidence of other activities on the site?
There are several plausible reasons for this enhancement.
The zinc values in the topsoil surrounding the henge are not
the highestin the field, with the southern part of the field being
consistently above 40 ppm. The soil type here has developed
on top of a chalk bedrock, with the rest of the field assumed to
be on a greensand geology. This assumption, from existing
soil maps, however has been found to not be a true represen-
tation of the soil variability within the field. Instead, there is a
much more complex situation with a band of greensand curv-
ing around the visible archaeological features in the geophys-
ical survey. The archaeological features themselves sit on top
of a chalk promontory that fluctuates in depth with a mixed
horizon boundary.
Therefore, it is possible that the elevated zinc concentration
is coming from these chalk-based topsoils, but the digging of
the henge has increased the amount of mixing of soil horizons,
creating the affect that the henge shows more strongly than
other areas on similar chalk-based topsoils. Adding in further
complexity is a number of pits that surround the henge iden-
tified in aerial photographs and the geophysical survey and the
possibility of other sources of zinc inputs into the soil (such as
through manures and other activities). Due to only topsoil
samples being studied in this instance, the mixing of the soil
horizons could mean several different sources for this varia-
tion in zinc and until further research evaluates this in more
detail, no firm conclusions can be made.
Yet, this example demonstrates the complex situation that
archaeological sites present in relation to their effect on soil
geochemistry and their relationship with complex soil varia-
tions; it also shows the potential benefits of high-resolution
data that archaeologists have collected for our understanding
of soil variations on an agriculturally relevant scale.
Concluding remarks
The growth and future of the global agricultural industry will
almost certainly rely on better use of technology and better
understanding of what farmers do, how they do it and what
impacts that has. As part of this, knowledge of the soils that
underpin crop growth and the archaeological remains that add
to the variability of soils is crucial. From examples presented
here, there seem to be many common interests between the
archaeological and agricultural communities in the UK, with
great potential for building archaeological evidence into mod-
ern agricultural management regimes. It must also be
recognised that this is not limited to just the UK; agricultural
technology is becoming far more prevalent from South
America to Asia, and the underlying connections between
archaeological sites, soils and agricultural management will
still exist, be it within different archaeological and agricultural
systems.
However, there are also some significant areas that warrant
further discussion and research. Not only is there a need to
understand the causes of variability on such case study sites,
whether archaeological or not, but also on practical aspects of
accessing and using this data responsibly. Primarily, these
ideas rely on the basis of access to data from farmers and
agricultural companies. Depending on who and how this data
gets used, this sort of access may prove complicated in situa-
tions where the locations of vulnerable archaeological sites
may need to be restricted, or where a farmersdatamightbe
confidential. On an individual case by case basis, archaeolo-
gists aware ofthese datasets could simply ask farmers whether
they have any potentially useful data for example. But if con-
sidering this as a large-scale approach to join datasets up,
dealings between agricultural companies and archaeological
bodies/companies may require different approaches with
clearly set out objectives and formal agreements.
This having been said, it may be that in some cases, farmers
may not want to understand more about the archaeology in
their soil for fear of interference in their farming operations for
example. While certainly possible, it is also possible that
where important sites have been designated to protect them,
there is little actual evidence for their extents, meaning over
restricting areas with no archaeological significance. In which
case, it may be possible to work more collaboratively to im-
prove the farmersability to grow crops and to help inform
better archaeological decision-making.
For these questions to be answered, it is necessary that
more future research is targeted at building better case studies
732 Archaeol Anthropol Sci (2019) 11:727734
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and an evidence base with which these ideas can be tested. It is
hoped that this paper has described some of the potential be-
tween archaeology and precision farming and promoted the
discussion of these ideas across many archaeological
communities.
Acknowledgements This research is beingconducted as part of an Arts
and Humanities Research Council (South West and Wales Doctoral
Training Partnership) PhD studentship. We wish to thank the many peo-
ple who have contributed time and advice to this research from the farm-
ing community, the precision farming industry and Historic England.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
References
Allred B, Daniels J, Mohammed RE (2008) Handbook of agricultural
geophysics. CRC Press, Florida
Green, A., 2014 A multi-disciplinary investigation of large oval enclo-
sures within Wessex. Dissertation (MSc in Applied Sciences by
Research). Bournemouth University. Unpublished
Heege JH (2013) Precision in crop farming: site specific concepts and
sensing methods: applications and results. Springer, Dordrecht
Joint Research Centre (JRC) of the European Commission and the
Monitoring Agriculture Resources (MARS) Unit H04 2014
Precision agriculture: an opportunity for EU farmerspotential
support with the CAP 20142020, Policy Department B:
Structural and Cohesion Policies, Agricultural and Rural
Development, European Parliament.
Lamb DW, Brown RB (2001) Review paper: remote-sensing and map-
ping of weeds in crops. J. Agric. Eng Res 78(2):117125 doi:
10.1006
Lambert JB (1998) Traces of the past: unraveling the secrets of archaeol-
ogy through chemistry. Perseus Publishing, USA
Linford N, Linford P, Payne A (2015) Chasing aeroplanes: developing a
vehicle-towed caesium magnetometer array to complement aerial
photography over three recently surveyed sites in the UK. Near
Surf Geophys 13(6):623631
McBratney A, Whelan B, Thomir A, Bouma J (2005) Future directions of
precision agriculture. Precis Agric 6:723
Mulla DJ (2013) Special issue: sensing in agriculture review twenty five
years of remote sensing in precision agriculture: key advances and
remaining knowledge gaps. Biosyst Eng 114:358371. https://doi.
org/10.1016/j.biosystemseng.2012.08.009.
Oliver MA, Bishop T, Marchant B (2013) Precision agriculture for sus-
tainability and environmental protection. Routledge, Oxon
Parliamentary Office for Science and Technology 2015 Precision farm-
ingPOSTnote 505, http://researchbriefings.parliament.uk/
ResearchBriefing/Summary/POST-PN-0505 accessedon7
November 2016
Seelan SK, Laguette S, Casady GM, Seielstad GA (2003) Remote sens-
ing applications for precision agriculture: a learning community ap-
proach. Remote Sens Environ 88(12):157169. https://doi.org/10.
1016/j.rse.2003.04.007
Stafford JV (2000) Implementing precision agriculture in the 21st centu-
ry. J Agric Eng Res 76(3):267275
Whelan B and McBratney A 2003 Definition and interpretation of poten-
tial management zones in Australia, Proceedings of the 11th
Australian Agronomy Conference,pp.26. Available at: www.
usyd.edu.au/su/agric/acpa (Accessed: 14 November 2016)
Case study site in Wiltshire, UK
NGR SU092573
Fig. 5 Caesium magnetometer
survey and interpretation courtesy
of Historic England (Linford et al.
2015) (left) and the variation in
zinc within the topsoil at the same
site (right)
Archaeol Anthropol Sci (2019) 11:727734 733
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Wilson CA, Davidson DA, Cresser MS (2008) Multi-element soil analy-
sis: an assessment of its potential as an aid to archaeological inter-
pretation. J Archaeol Sci 35(2):412424. https://doi.org/10.1016/j.
jas.2007.04.006
Zhang N, Wang M, Wang N (2002) Precision agricultureaworldwide
overview. Comput Electron Agric 36(2):113132
http://www.ipf-uk.com/precision-farming/soil-zoning/soil-brightness.
html accessed 27 February 2016
http://www.nesta.org.uk/blog/precision-agriculture-separating-wheat-
chaff accessed on 7 November 2016
https://sa.catapult.org.uk/documents/10625/53165/The+Courtyard+
Partnership+case+study/26073572-f15f-41ae-8480-9857b682e84e
accessed 27 February 2016
734 Archaeol Anthropol Sci (2019) 11:727734
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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... Over the last 30 years, concepts of precision farming (PF) [8] have been developed that help farmers to understand yield variability within their fields in order to adjust N application. Generally, PF uses different technologies like global positions systems, yield mapping, soil conductivity measurements [9] or non-contact spectral sensors for monitoring and determination of e.g., N status of different field crops [8]. ...
... Over the last 30 years, concepts of precision farming (PF) [8] have been developed that help farmers to understand yield variability within their fields in order to adjust N application. Generally, PF uses different technologies like global positions systems, yield mapping, soil conductivity measurements [9] or non-contact spectral sensors for monitoring and determination of e.g., N status of different field crops [8]. These spectral sensors are based on the principle of reflectance and changes of electromagnetic radiation between 300 and 2500 nm [10] and can be ground-borne, airborne, or space-borne [11]. ...
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The determination of plant nitrogen (N) content (%) in wheat via destructive lab analysis is expensive and inadequate for precision farming applications. Vegetation indices (VI) based on spectral reflectance can be used to predict plant N content indirectly. For these VI, reflectance from space-borne, airborne, or ground-borne sensors is captured. Measurements are often taken at the canopy level for practical reasons. Hence, translocation processes of nutrients that take place within the plant might be ignored or measurements might be less accurate if nutrient deficiency symptoms occur on the older leaves. This study investigated the impact of leaf number and measurement position on the leaf itself on the determination of plant N content (%) via reflectance measurements. Two hydroponic experiments were carried out. In the first experiment, the N fertilizer amount and growth stage for the determination of N content was varied, while the second experiment focused on a secondary induction of N deficiency due to drought stress. For each plant, reflectance measurements were taken from three leaves (L1, L2, L3) and at three positions on the leaf (P1, P2, P3). In addition, the N content (%) of the whole plant was determined by chemical lab analysis. Reflectance spectrometer measurements (400–1650 nm) were used to calculate 16 VI for each combination of leaf and position. N content (%) was predicted using each VI for each leaf and each position. Significant lower mean residual error variance (MREV) was found for leaves L1 and L3 and for measurement position on P3 in the N trial, but the difference of MREV between the leaves was very low and therefore considered as not relevant. The drought stress trial also led to no significant differences in MREV between leaves and positions. Neither the position on the leaf nor the leaf number had an impact on the accuracy of plant nitrogen determination via spectral reflectance measurements, wherefore measurements taken at the canopy level seem to be a valid approach.
... Multi-spectral imaging devices capture image data within specific wavelength ranges, and these are commonly used to assess vegetation coverage and health, including the health of crops (Candiago et al. 2015). Since crop health can be influenced by the characteristics of subsurface deposits, multi-spectral imaging devices have the potential to be used for archaeological prospection in crop paddocks (Webber et al. 2017). For example, crop health is likely to be reduced if subsurface features are limiting root penetration (e.g. if structural features or foundations are present). ...
... Conversely, crop health is likely to be improved if previous disturbance has resulted in less compact sediments, which facilitates root growth (e.g. if previous 'cut and fill' activities have been undertaken). Subsequently, any irregularities identified in crop health can be examined for patterns revealing subsurface archaeological features, such as stone wall and building foundations, or previous earthwork activities (Webber et al. 2017). This approach to archaeological prospection can be quicker and easier than geophysical prospection techniques, such as magnetic gradiometry, but it can only be applied to study areas characterised by uniform vegetation profiles, such as crop paddocks. ...
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Drone technology provides a relatively quick and inexpensive method for capturing images and other data from the sky. With the help of dedicated software, the information captured with a drone can be applied to produce a range of outputs with the potential to contribute to the identification, study and management of cultural heritage. This paper presents an overview of some of the approaches that can be employed and presents an example from some research currently underway on Bunurong Country in southeastern Australia. As part of a cultural heritage management project, a 3D model of part of an inland dune system has been produced prior to the landscape being modified for agricultural development. Creating digital archives of this nature allows community members and researchers access to more detailed information about the context in which cultural heritage has been identified than would otherwise be possible. This is a particularly important outcome when the landscape itself is unable to be preserved.
... Precision agriculture techniques are creating software and workflows that use remote sensing principles to asses crops health. All these elements are drastically changing the panorama of aerial archaeology, whereby aerial images obtained from UAVs are now widely used for survey, recording and publication [19][20][21][22][23][24][25][26]. ...
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... PA or PF is generally defined as information and technology based farm management system to identify, analyse and manage spatial and temporal variability within fields for optimum productivity and profitability, sustainability and protection of the land resource by minimizing the production costs. Increasing environmental consciousness of the general public is necessitating us to modify agricultural management practices for sustainable conservation of natural resources such as water, air and soil quality, while staying economically profitable (Sonka and Cheng, 2015;Webber et al., 2017). Stakeholders, such as farmers, seed producers, machinery manufacturers, and agricultural service providers are trying to influence this process (Schönfeld et al., 2018). ...
Conference Paper
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People are considered food secure when they have access, at all times, to sufficient, safe, nutritious food to maintain a healthy and active life-the absence of these conditions is known as food insecurity. The latest global estimates indicate that 795 million, around one of every nine people, are hungry and that of under nutrition prevalence, which measures the proportion of people unable to consume enough food for an active and healthy life, is 12.9% in developing regions. Challenges of today and tomorrow of doubling food supply put sustainability of agriculture at level with ensuring food security. The global food system needs to be resource efficient and at the same time sustainable. Implementing agricultural innovations is key for coping strategies in the contexts of climate change and food security. Precision Agriculture (PA) or Precision Farming (PF) includes innovative agricultural management practices that serve these purposes. PA has been greatly promoted for the potential of high-tech tools to sustainably intensify food production through increasing yields and profits, decreasing the environmental impacts of production, and improving food safety and transparency in the food system through the data collected by precision agriculture technologies. However, little is known about how farmers use PF technologies to support managerial decision-making, or about the relative magnitude of benefits and costs of PF technologies on individual farms. The aim of this study is to assess the PA practices that have been implemented since the 1980s in the world in terms of food security. Moving from the experience of various countries, it has been discussed innovation challenges in the PA for both crop and animal production. In addition, ongoing work on the PA in Turkey is evaluated in terms of the needs of the future.
Thesis
The crucial question in this thesis was how can remote sensing data and crop models be used to derive a N fertilizer strategy that is capable to lower the environmental side effects of N fertilizer application. This raised the following detailed objectives: The first objective (i) how N content determination via spectral reflectance is influenced by different leaves and positions on the leaf was investigated in Publication I. Different wheat plants were cultivated under different N levels and under drought stress in two hydroponic greenhouse trials. Spectral reflectance measurements were taken from three leaves and at three positions on the leaf for each plant. In total, 16 vegetation indices broadly used in the literature were calculated based on the spectral reflectance for each combination of leaf and position. The plant N content was determined by lab analyses. Neither the position on the leaf nor leaf number had an impact on the accuracy of plant N determination via spectral reflectance measurements. Therefore measurements taken at the canopy level seem to be a valid approach. However, if other stress symptoms like drought or disease infection occur, a differentiation between leaves and positions on the leaf might play a more crucial role. Publication II dealt with the second objective on (ii), how to incorporate leaf disease into the DSSAT wheat model to enable the simulation of the impact of leaf disease on yield. An integration of sensor information in crop growth models requires the update of model state variables. A model extension was developed by adding a pest damage module to the existing wheat model. The approach was tested on a two-year dataset from Argentina with different wheat cultivars and on a one-year dataset from Germany with different inoculum levels of septoria tritici blotch (STB). After the integration of disease infection, the accuracy of the simulated yield and leaf area index (LAI) was improved. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). A sensitivity analysis also showed a strong responsiveness of the model by the integration of different STB disease infection scenarios. Increasing the modeling accuracy even further a MM approach seems to be suitable. Assembling more models increases the complexity of the simulation and the involved calibration procedure especially if the user is not familiar with all models. To avoid these conflicts, Publication III evaluated the third objective (iii) if an automatic calibration procedure in a MM approach for winter wheat can eliminate the subjectivity factor in model calibration. The model calibration was performed on a 4-yr N wheat fertilizer trial in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-Index 0.93) and evaluation dataset (d-Index 0.81). This lead to the fourth (iv) objective to use a MM approach to improve the overall modeling accuracy. The evaluation of a fertilizer trial showed an improved modeling accuracy in most cases, especially in the drought season 2018. Based on the combination of a MM approach and the incorporation of sensor data, a Nitrogen Application Prescription System (NAPS) was developed. The initial NAPS setup requires long term recorded data (yield, weather, and soil) to ensure proper MM calibration. After calibration, the current growing season conditions are required (weather, management information) until the N application date. Afterward, the NAPS incorporates remote sensing information and generated weather for running future N application scenarios. The selection of the proper amount of N is determined by economic and ecological criteria. Furthermore, in order to account for differences in in-field variabilities and to deliver a N prescription site-specifically, the NAPS concept has to be applied on a geospatial scale by adjusting soil parameters spatially. The NAPS concept has the potential to adjust the N application more economically and ecologically by using current sensor data, historical yield records, and future weather prediction to derive a more precise N application strategy. Finally, this concept exhibits the potential for reconciliation of the issue of an economic, agricultural production without harming the environment.
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Precision Agriculture is advancing but not as fast as predicted 5years ago. The development of proper decision-support systems for implementing precision decisions remains a major stumbling block to adoption. Other critical research issues are discussed, namely, insufficient recognition of temporal variation, lack of whole-farm focus, crop quality assessment methods, product tracking and environmental auditing. A generic research programme for precision agriculture is presented. A typology of agriculture countries is introduced and the potential of each type for precision agriculture discussed.
Book
High yields and environmental control in crop farming call for precise adaptations to local growing conditions. Treating large fields in a uniform way by high capacity machinery cannot be regarded as a sustainable method for many situations. Because differences existing within single fields must be considered. The transition from former field work carried out manually or by small implements to present-day high-capacity machinery caused that the farmers lost the immediate and close contact with soils and crops. However, modern sensing and controlling technology can make up for this deficit. High tech methods that include proximal sensing and signals from satellites can provide for controls that allow adjusting farming operations to small fractions of one ha and sometimes even down to some m2, hence in a site-specific mode. This applies to operations for soil cultivation, sowing, fertilizing and plant protection. This book deals with site-specific concepts, applications and results.
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Book
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