PUTTING SENSORS TO WORK:TARGETED APPLICATION OF NUTRIENTS AND PESTICIDESLITERATURE REVIEW AND RESULTS FROM A SURVEY

PUTTING SENSORS TO WORK: TARGETED APPLICATION OF NUTRIENTS AND PESTICIDES (TARG APP)LITERATURE REVIEW AND RESULTS FROM A SURVEY Task 1.1 Desk-study

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November 2017

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Contents

ABOUT THIS REPORT ................................................................................................................................................. 2

INTRODUCTION TO SENSOR-CONTROLLED VARIABLE RATE APPLICATION (VRA) ...................................................... 3

TOOLS FOR VRA ........................................................................................................................................................ 4

SATELLITE IMAGES .............................................................................................................................................................. 5

CROPSAT ......................................................................................................................................................................... 6

APPS FOR PREDICTION OF N-STATUS ...................................................................................................................................... 7

VEGETATION INDEXES ......................................................................................................................................................... 7

VRA OF FERTILISER - REVIEW .................................................................................................................................... 9

SCIENTIFIC LITERATURE ON VRA OF NITROGEN....................................................................................................................... 10

EXPERIENCE OF VRA OF PESTICIDES PRESENTED IN FARMER JOURNALS ........................................................................................ 10

SCIENTIFIC LITERATURE ON VRA PLANT PROTECTION ............................................................................................................... 11

RESULTS FROM SURVEYS ON PRECISION AGRICULTURE IN SWEDEN, DENMARK AND FINLAND ............................. 12

CONCLUSIONS REGARDING BARRIERS FOR USE OF CROP SENSORS ........................................................................ 14

CAN WE IDENTIFY BEST PRACTICE OF CROP SENSOR USAGE? ................................................................................. 14

REFERENCES ............................................................................................................................................................ 15

APPENDIX 1. DANISH SURVEY ON CURRENT USE OF CROP SENSORS AND BARRIERS FOR USE ................................ 18

ABSTRACT ...................................................................................................................................................................... 18

INTRODUCTION ................................................................................................................................................................ 18

METHOD ........................................................................................................................................................................ 18

RESULTS ......................................................................................................................................................................... 18

DISCUSSION .................................................................................................................................................................... 32

About this report

This report is a deliverable in the ICT-AGRI funded project ‘Targ-App’. It addresses the part of the

project which aims to identify ‘best practice’ and barriers to implementation for crop sensor de-

ployment by farmers and map the practical barriers for use of crop sensors. The first part of the

report includes a review of agricultural newsletters and magazines from Denmark, Sweden and

Finland, scientific literature, and websites. The review on Finnish literature revealed limited infor-

mation regarding farmers’ experience with crop sensors. The second part of the report presents

results from a questionnaire, which was e-mailed, to selected Danish farmers in 2016.

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Introduction to sensor-controlled variable rate application (VRA)

Currently most farmers tend to apply the same treatment to the entire field, as most traditional

machinery is adjusted once for completing the whole field at one level. However, the same farm-

ers will also be aware that typically there are variations in yield within the field, which may be

caused by a range of factors. Sometimes farmers will adjust the treatment in a corner of the field

or at headlands if e.g. the crop is particular dense or there are patches with heavy weed infesta-

tions.

Variable rate application (VRA) aims to apply the optimum nitrogen rate based on the require-

ments observed within the field. The opportunity to measure the nitrogen requirement on crop

level is based on vegetation indexes.

Vegetation indexes like the Normalised Difference Vegetation Index (NDVI) correlates with a

range of plant parameters, e.g. biomass, leaf area index (LAI), content of chlorophyll as indicator

of nitrogen content in plants (Pettorelli, 2013).

Some tractor mounted crop sensors can apply variable rate of fertiliser and pesticides on the go

using integrated algorithms. The on-the-go function is unique for the tractor mounted crop sen-

sors. But for collecting vegetation index maps tractor mounted crop sensors are one out of sever-

al tools. Drones equipped with multispectral cameras also provide the possibility to collect infor-

mation on variations within fields. And recently the free access to satellite data has made VRA

assessable to a larger group of farmers.

The incentive to adopt crop sensor technology is the prospect of either increase in yield, better

utilisation of fertiliser, and/or reductions in the expenses regarding pesticides. The field variation

determines whether a site-specific approach is cost-effective or not. However, the field experi-

ments comparing uniform rate applications (URA) and VRA show inconsistent benefits in yield

and profit (as cited by Jørgensen and Jørgensen, 2007) and few farmers have yet adopted preci-

sion farming technologies as VRA from crop sensor readings.

Most farmers and advisers that have been interviewed for farmer journals regarding their experi-

ences with crop sensors are propelled by a common interest in technology (Østerlund, 2016, Kel-

strup, 2016, Petersen and Andersen, 2013, Hattesen, 2012a and Hattesen, 2012b). Some have

adopted GPS, yield monitoring and crop sensors as early as 1992 (Kelstrup, 2016). They are ac-

tively using the collected data e.g. by comparing yield maps from different years and thereby de-

tecting year to year changes in biomass variations within the field (Kelstrup, 2016). However they

constitute a small group of farmers, approx. 5 % in Denmark (Thierry et al., 2016).

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Tools for VRA

Table 1 lists technologies for crop sensing/generating vegetation index maps available in Denmark, Sweden and Finland.

Name

Yara N-sensor (YARA) Spectral reflectance crop sensor measuring in a

radius of 3 meters around the tractor, installed on

tractor roof.

Crop Sensor/ISARIA

(Claas/ Fritzmeier)

Spectral reflectance sensors installed on a boom

in front of a tractor.

Trimble GreenSeeker

(Geoteam)

Spectral reflectance crop sensor, optional number

of sensors on a tractor (disc spreader installed on

a boom in front of tractor) or on a sprayer boom

(liquid fertiliser use)

Trimble UX5 (Ge-

oteam).

Spectral reflectance crop sensor attached to a

fixed wing drone/UAV.

Satellites, CropSAT CropSAT is an online service delivering remote sensing of spectral reflectance based on data from the Sentinel satellites. NDVI maps and application maps are free to download.

CropSpec (TopCon) Spectral reflectance crop sensor measuring on

both sides of a tractor, two sensors installed on

tractor roof.

OptRx (AgLeader) Spectral reflectance crop sensor, optional number

of sensors installed on a boom in front of a tractor

(disc spreader use) or on a sprayer boom (liquid

fertiliser use).

Airinov Spectral reflectance crop sensor attached to a

fixed wing drone/UAV.

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Since 2010 Danish farmers have been able to apply for subsidy when investing in certain envi-

ronmental technologies e.g. crop sensors. The subsidy scheme is part of the rural development

programme and in the period from 2010 - 2012 it was possible to apply for 40 % investment sub-

sidy for new advanced sprayers with crop sensors and other tools, for reduction of nutrient losses

and greenhouse gas emissions (Christensen, 2015). From 2013 the subsidy scheme was rede-

signed to provide financial support for the sensor equipment only, and not the whole sprayer sys-

tem (Elbæk, 2015). Around 213 farmers have applied for funding under the subsidy scheme (Na-

turErhvervstyrelsen, unpublished). It is unknown, to which extend the sensors are used when ap-

plying fertiliser and plant protection.

In Sweden the most preferred crop sensor is the Yara N-Sensors and 180 farmers have invested

in this sensor (Nissen, 2016).

In Finland approximately 10-15 farmers have invested in sensor equipment. In Finland as well as

in Sweden, Yara N-sensor is the market leader and there are about ten contractors who use it.

Geotrim is another company active in fertiliser sensors (GreenSeeker). Mr Ylikleemola (farmer

and Yara N-sensor contractor) has informed us that VRA is done in connection to nitrogen-

fertilisation and to some degree when spraying for grain leaf diseases (eye inspection). Less fun-

gicide is applied to areas which appear as if the yield will be low (P. Rajala, personal communica-

tion, June 2016).

Since satellite data has become freely available and services like CropSat has been developed

more farmers have access to NDVI data for their fields and thus they are not dependent on tractor

mounted sensors for VRA. About 3% of all Danish farms have used vegetation indices originated

from satellites (85%) or drones (18%) in the period from May 2016 to May 2017. The satel-

lite/drone was mainly used for variable fertiliser rate mapping (44%). Less common was the map-

ping for variable rate of plant protection (16%) and seed (6%). The majority of users (64%) indi-

cates that they use satellite/drones for other management purposes, e.g. monitoring, drainage,

liming or to support the making of cultivation plans (Statistics Denmark, 2017). The survey did not

provide any statistics on the use of machine mounted crop sensors in specific.

Satellite images

After the European Space Agency (ESA) launched the satellite Sentinel 2, remote sensing of

plant growth from satellites has been re-introduced as freely available web applications to provide

fertilisation prescription maps. The satellite Sentinel 2 was launched in June 2015 and its main

task is to measure reflections from the surface, including reflection from the crops. Reflectance

measurements can be used as input for the various vegetation indices. The Sentinel 2 satellite

program is identical to the known Landsat, and has the following capabilities;

i) carries an innovative wide-swath, high-resolution, multispectral imager (MSI) measur-

ing reflectance on 13 wave bands in the VIS (whereof three bands in the red-edge)

and NIR (whereof two bands in the short wave infra-red) of the spectrum,

ii) systematic global coverage of land surfaces from 56° S to 84° N latitude,

iii) revisiting every 5 days, and

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iv) Delivers spatial resolution down to 10 m. The reflectance measurements provided by

the Sentinel 2 satellite is free to use in its raw data format. Atmospheric corrections

have to be done in order to make the spectral reflectance measurements usable.

Mainly due to the high data volumes, the atmospheric correction of raw data into multi-

spectral imaging will not be performed by the ESA operated ground segments, neither

the generator of vegetation indices image representation. Both will rely on software

packages to be run by the users on the raw data sets. It has become a business case

to manage the complexity of ortho-rectification down to field boundary level, provide

vegetation reflectance indices, handling imagery archive and make the archive availa-

ble for end-users via easy-to-integrate web services. Corrections also include the or-

tho-rectification and spatial registration on a global reference system (combined UTM

projection and WGS84 ellipsoid) with sub-pixel accuracy.

CropSAT

Dataväxt in Sweden has developed the web application CropSat (cropsat.se) which processes

multispectral data from satellites. In 2016 CropSat has been made available to Danish farmers as

well (). In 2017 4100, 7300 and 1500 new users visited CropSat in Sweden, Denmark and Nor-

way, respectively.

Users of CropSat can prepare a fertiliser application map both at field level and subfield level. The

fertiliser prescription map can be exported to a range of spreader terminals and task controllers

that support the CropSat prescription map file format. The practical experience is that cloud free

vegetation index data for fields in Denmark and in south Sweden was obtained 1 to 2 times per

month beginning in April until August (source www.cropsat.dk and www.cropsat.se).

Figure 1: Screen print from CropSAT.se. Files for variable rate application can be generated and downloaded

from the web based program CropSAT. Farmers in Sweden, Denmark and Norway can use CropSat.

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The vegetation indices derived from satellite remote sensed foliage reflections will not necessarily

match completely the data output from the above described ground vehicle mounted sensors.

However, satellite sensed reflection data from crop foliage is valuable for making decisions within

and between fields. The resolution is 10 x 10 m and data is free. This open data policy of the EU

Sentinel programme opens for innovative solutions like CropSat, AgriSat and other programmes

which are promising, low cost and practical adaptable (i.e. uncomplicated and at hand) tools for

crop management.

Recently published vegetation indices like the Normalized Area Over reflectance Curve (NAOC)

and the Double-peak Canopy Nitrogen Index (DCNI), combined with biomass indices show prom-

ising results on assessment of crop nitrogen status (Delegido et al., 2010; Chen et al., 2010).

Apps for prediction of N-status

Further the remote sensing of crop nutrient status has recently become of interest in relation to

App’s for smart phones, and other smart devices equipped with high resolution digital cameras.

The digital cameras of standard devices can only store the reflection in the visual spectrum. The

user is required to walk around the field to locate patches with divergent foliage mass and/or col-

our. The app captures images and through on-line communication a vegetation index based on

the visual spectrum is instantly computed. An example is the Dark Green Color Index (DGCI) of

plant leaves that utilise the image colour space (hue, saturation and brightness) (Karcher & Rich-

ardson, 2003; Rorie et al., 2011). This visual spectrum based vegetation index has been correlat-

ed to a database of direct chlorophyll meter readings (SPAD) of different crop species at different

growth stages and at different nitrogen uptake (database is not complete yet for all crop species).

The App outputs the nitrogen uptake and/or nitrogen contents of the crop. The information can be

used to decide on optimum fertiliser rates in average of the field. There are no current compre-

hensive agronomic evaluation of the available App’s from YARA ImageIT© and FieldScout©. Dif-

ferences in light conditions, camera quality, and available camera settings could affect DGCI-

readings and limit their utility in diagnosing nitrogen deficiencies. Similar to other remote sensing

technologies, factors such as disease, water status and deficiencies in other nutrients than nitro-

gen may affect the DGCI-readings.

Vegetation indexes

NDVI-data based on sensor readings from satellite, drone or a vehicle mounted sensor provides

information about the reflectance from different wave-length bands only. In order to practice VRA

the biomass images is processed into application maps based on vegetation indexes.

Vegetation indexes describe plant chlorophyll content, which correlates with plant nitrogen con-

tent. Nitrogen availability is crucial for crop development and quality. Nitrogen availability in soil

differs not only between different soil types. Climatic variations also cause differences in nitrogen

availability. Precipitation and temperature affect the mineralisation of nitrogen and soil water con-

tent affects the biological activity and thereby also nitrification and denitrification. The previous

crop also has a significant influence on nutrient availability. A vegetation index like NDVI give an

indication of photosynthetic activity which correlates with the amount of nitrogen in the plant. This

correlation has been extensively studied and verified in many crop studies (Pettorelli, 2013).

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The vegetation-index values express reflectance (ρ) (where ρ is reflected wavelengths in nm ())

of R (red light) and NIR (near infrared light) (Söderstrøm et al., 2016). No crop sensor measures

nitrogen content or nitrogen demand directly, however, correlations with sensor readings are of-

ten good.

NDVI (equation 1) was initially designed for satellites to measure the reflection from vegetation on

the surface of the Earth. The index is calculated by sensors measuring both the R and NIR reflec-

tance. Chlorophyll absorbs R whereas NIR is reflected by a dense fertile crop.

𝑁𝐷𝑉𝐼 =(𝜌𝑁𝐼𝑅−𝜌𝑅)

(𝜌𝑁𝐼𝑅+𝜌𝑅) (equation 1)

Pixels values range from -1 and 1 in NDVI. Thus, areas covered by vegetation vary between

close to zero and close to one. Based on the NDVI scale the photosynthesis activity can be esti-

mated (Weier and Herring, 2000).

Figure 2: Differences in NDVI-value is an indicator for biomass density. The picture is derived from the freely available Danish programme CropSAT.dk, which is based on the Swedish CropSAT-programme, which was originally developed within the framework of Precision Farming Sweden (POS).

It is well known that the standard NDVI loses sensitivity when the leaf area index (LAI) exceeds

about 2.0–2.5 (Heege et al., 2008, Vinã et al., 2011). A well-developed cereal crop can have a

LAI of up to 8.0. Therefore the NDVI may miss important information about the nitrogen supply for

a dense cereal crop at e.g. the 2nd fertiliser application (Heege et al., 2008). Nitrogen deficiency

within normally developed foliage may be compensated by increased fertilisation, whereas it cer-

tainly is not reasonable to apply high amounts of nitrogen to larger patches of bare soil. For ex-

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ample, the ratio of near infrared - to visible red light reflectance can change from 20 to 3 on winter

killed patches, where the crop sensor then should be able to differentiate between nutrient defi-

ciency and winter killed patches. In many situations, the crop sensor application is mostly limited

to the second and/or later fertiliser applications. Some crop sensor brands also offer the output of

the red edge inflection point (REIP) calculated by the approximating generic formula:

𝑅𝐸𝐼𝑃 = 700 + 40 ((𝜌670−𝜌780) 2⁄ −𝜌700

𝜌730−𝜌700) (Equation 2)

Where are reflected wavelengths (number added to is wavelength in nm) in percentage.

This provides the best relationship to nitrogen supply to crop plants (reviewed in e.g. Heege et al.,

2008). When chlorophyll in plant leaves degrades, less absorption in the red light spectrum leads

to a shift towards the blue spectrum. The above index for REIP (Dawson & Curran, 1998) is af-

fected by biochemical and biophysical parameters and has been used as a mean to estimate foli-

ar chlorophyll or nitrogen content (the higher the number, the greater the amount) (Vinã et al.,

2011). Distortion of crop sensor measurements derived from reflections from other plant species

like weeds, drought stress, diseases, and other nutrient deficits than nitrogen are common. Most

current sensor units have an active illumination source which reduces influence from changing

ambient light conditions and may extend the operational hours. It furthermore eliminates the noise

associated with the varying irradiance of natural light and shade effects caused by the machinery

or surroundings. Technically, the artificial light needs to be modulated in order to separate the

artificial light from the natural sunlight.

VRA of fertiliser - review

Experience on VRA fertilisation presented in farmer journals

The experiences of farmers who have adopted sensor technology has been presented in farmer journals.

VRA of nitrogen fertiliser was tested on 300 ha winter wheat. The resulting increase in yield

equalled 1 hkg/ha (Elbæk, 2011, Knudsen and Hørfarter, 2009, Andersen, 2003, Pedersen (red.)

2002, Pedersen (red.) 2001). The effect on yield was assessed to be influenced by the Danish

nitrogen-norm policy at the time of the experiment. Allocating nitrogen from one part of the field to

another has a strong impact on the nitrogen-yield response curve when nitrogen input is already

at least 10% below optimum (Thalbitzer, 2005). The increase in nitrogen-input to Danish crops up

to economic optimum is expected to improve the effects from VRA. The VRA based on biomass

recordings can, furthermore, potentially reduce leaching of nutrients by optimal allocation of ferti-

liser (Knudsen and Hørfarter, 2011 and Delin et al., 2015).

A Swedish farmer used the sensor to monitor the effect of the autumn fertilisation with manure in

oilseed rape in order to save nitrogen and distribute the nitrogen to the parts of the field where it

was needed. This saved approx. 23 % of the nitrogen dosage to the fields. The farmer also ob-

served that oilseed rape had a more even flowering. This farmer had overcome the challenge of

unprecise sensor readings due to wilted leaves in oilseed rape by monitoring the field late autumn

and using these data for the spring VRA (Gunnarson, 2010).

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Scientific literature on VRA of nitrogen

Recent studies on application of nitrogen according to sensor-data at the field level do not show

statistically significant results on harvested yield (Jørgensen and Jørgensen, 2007; Boyer et al.,

2011; Bragagnolo et al., 2016).

However above ground crop nitrogen was found to vary around 100 kg N/ha within fields in unfer-

tilised cereal crops showing that variations within fields can be as large as between fields (Delin

et al., 2015).

Delin et al. (2015) quote a study where good correlation was found between the Yara N-sensor

value (SN value) and nitrogen in harvested grain from unfertilised plots using the handheld Yara

N-sensor and concludes that SN values from unfertilised plots are useful in predicting the opti-

mum nitrogen application when the potential yield is known.

Mayfield and Trengove (2009) made a detailed analysis where they compared grain yield re-

sponses in different biomass areas for wheat with uniform and variable nitrogen applications by

using an N-sensor. For each 20 % interval of biomass the difference in yield were compared.

They found the greatest yield increase (+2.8 hkg/ha) on areas with the lowest 20 % crop biomass

and a negative difference in yield for higher biomass intervals.

Besides yield increase, studies show higher nitrogen use efficiency (NUE) when nitrogen fertiliser

was based on sensor-data. NUE increases with 10% and 15-35 % for corn and winter wheat, re-

spectively (Raun et al., 2002; Li et al., 2008; Bragagnolo et al., 2016). Delin et al. (2015) reviewed

reduction in leaching of 0.5-3.8 kg N/ha and 0.2-1.6 kg N/ha on clay and sandy soils respectively

compared to uniform application and dependent on within field variation. These benefits indicate

that VRA of fertiliser according to the plant nutritional state can reduce the risk of fertiliser losses.

The review by Delin et al., 2015 found studies showing a reduction in leaching of 3.2-6.8 kg N/ha

(sandy soil) and 1.4-3.0 kg N/ha (clay soil) when going from fertilisation rates 10 kg/ha above av-

erage optimum to optimum fertilisation rates.

Experience of VRA of pesticides presented in farmer journals

Agricultural newsletters and magazines contain some articles about farmers and advisers, exper-

imenting with the use of crop sensors. Examples of sensor-use are shown below:

- For VRA of PGRs (Plant Growth Regulators) and fungicides in winter wheat (Bröker, 2011)

- For identification of areas with high vegetation-index values shortly before harvest in order

to desiccate weed and unripe cereal thereby obtaining a more homogeneous crop for har-

vest (Hattesen, 2012a).

- For registration of biomass in autumn. Using registrations for VRA of herbicides in early

spring (Hattesen, 2012b).

- Using maps generated by crop sensors to identify areas of special attention. Here vegeta-

tion maps are integrated as part of the overall planning of nitrogen-allocation (Østerlund,

2016).

- For PGR application

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A field trial which tested VRA for plant protection showed increases in yield of up to 1 hkg/ha for

winter cereals. In the trial, areas with dense biomass received up to 20 % higher dose and vice

versa. The field trial was carried out by LandboNord (Pedersen (red.), 2009).

In the northern part of Denmark, approx. 25 farmers participated in a learning group to learn how

to use sensor guided sprayers at their farms. After the installations of a sensor and test-runs,

about 20-25 % of the farmers used VRA as an integrated part of their management system.

Farmers found the new sprayers technically challenging to use. In one case a farmer had learned

to use the sensor guided application system, but when the son took over the field management,

he experienced a technical problem and stopped using the VRA function. Another farmer who

participated in the learning group had experienced the same problem and solved the obstacle

within a few minutes (Elbæk, personal communication, July 2016). This experience illustrates the

wide range of challenges that arise when implementing sensor technology in the crop production

and the value of timely support.

One farmer who used VRA for fertilisation and plant protection stated, that he saved 5-8 % of the

pre-harvest glyphosate application by using sensor based VRA, but for fungicides the overall

dosage was the same although the applied dose varied up to 40 % (Kelstrup, 2014).

The activities listed below show VRA of plant protection operations that are profitable due to ei-

ther reduced pesticide use, increases in yield, or both (Petersen, 2013).

Table 2: Operations where VRA of pesticides are profitable (Petersen, 2013).

Operation Potential reduction in pesticide use Potential yield increase

Glyphosate prior to harvest 10-20 % 0

Fungal attack 0 % 0.5-1 %

Growth regulation 5-10 % 0.5-1 %

Controlling weed in winter cereals 5-10 % 0-?

Desiccation in potatoes 20-30 % 0

Scientific literature on VRA plant protection

A brief review of the scientific literature supports the findings in Tab. 2 by indicating that there is

considerable pesticide saving to be gained from sensor-based VRA on heterogeneous crops. For

fungicides, growth regulators and desiccants, the low-biomass areas are sprayed with reduced

dosages of pesticides because a smaller plant area needs to be covered compared to high-

biomass areas. Therefore, in heterogeneous fields with large patches of low-biomass areas, the

savings can be considerable. A one-year study by Tackenberg et al. (2016) and another by

Dammer et al. (2009) showed 10-20 % savings of fungicides in winter wheat by using sensor-

based data. Disease infestation level was assessed after a fungicide application in winter wheat,

the experiment showed that VRA had the lowest disease infestation level with flag leaves: 6 %

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infestation (uniform 12 % infestation), and the first leaves: 32 % infestation (uniform 42 % infesta-

tion) (Tackenberg et al., 2016). A five-year study show similar results, however higher fungicide

savings, on average 22 %, were achieved when using variable rate spraying in winter wheat

(Dammer & Ehlers, 2006). Dammer and Wartenberg (2007) combined a standard patch sprayer

with similar technology as the GreenSeeker for weed sensing in tramlines of growing cereals and

peas fields, and achieved an average herbicide saving of 24.6 % and no yield reduction com-

pared to conventional application (dominant weed species in this study were couch-grass (Ag-

ropyron repens), common lambs quarter (Chenopodium album), dead-nettle (Lamium spp.),

bindweed (Polygonum convolvulus) and knotgrass (Polygonum aviculare)).

For herbicides, similar savings can be found in the literature. Herbicides savings of 20-48 % are

documented for corn and 23-28 % for wheat and peas, respectively (Williams & Mortensen, 2000;

Carrara et al., 2004; Dammer & Wartenberg, 2007). Carrara et al. (2004) found a more even grain

yield across the field when using variable-rate application of herbicides compared to uniform ap-

plication, demonstrating that variable-rate application was effective to produce an even crop.

Several field trials have applied site-specific weed management in fields with weed infested

patches (reviewed in Miller, 2003; Christensen et al., 2009). In some studies, the effect of the size

of treated area (treatment resolution) has been investigated; typically the minimum treatment unit

is 3 m x 3 m (Barosso et al., 2004) or 1 m x 1 m (Paice et al., 1998). Wallinga (1998) found that

potential herbicide saving increased with increasing spatial resolution to smaller areas of weed

control.

In two of three years, in field trials with winter wheat increased doses of fungicides, an interaction

of crop biomass and fungicide dose rate had a significant influence on disease severity indicating

a biomass dependent dose response. The interaction occurred in the two years with high yield

potential in combination with severe disease attack (Jensen & Jørgensen, 2016).

None of the scientific studies showed an effect on crop yield; however the demonstrated pesticide

savings ranged between approximately 20-40%. This indicates that VRA of pesticides can reduce

pesticide use and reduce the costs without affecting crop yields.

A recent study with VRA of PGS’s showed a yield benefit on three of four fields compared to the

farmers best uniform practice (Griffin & Hollis, 2017).

Results from surveys on precision agriculture in Sweden, Denmark and Finland

A Swedish study (Söderström et al., 2013) on farmers’ experiences with Yara N-sensors showed

that at least 250 ha were minimal for return of investing in the sensor equipment for fertilisation. In

this study sensors were available to farmers through contractors who did the scanning and fertili-

sation. A survey showed that almost 70 % of the farmers experienced that the VRA resulted in

increased yields and more uniform protein content, and approx. 80 % saw less lodging in the N-

sensor fields.

A Norwegian master thesis (Helmen, 2012) included a questionnaire sent to 9 Swedish farmers

and 1 Norwegian farmer who had invested in a Yara N-sensor. The study concluded that the re-

duced risk of lodging, high uniform protein content and higher yields were stated as the main

13 / 33

benefits of using an N-sensor for nitrogen fertilisation. The system was also expected to increase

the harvest capacity by producing a more uniform crop. Three of ten farmers used the same ferti-

lisation amount as before they switched to VRA, five had reduced the fertiliser amount by 4-7 %,

and two had reduced the fertiliser amount by 10 %. The thesis also investigated technical prob-

lems and on average the 10 farmers experienced 1.2 stops per season because of failures in the

N-sensor system. Regarding user support, the farmers seemed quite happy with the technical

support provided by the supplier.

A survey in Germany, Finland and Denmark from 2012 (Bligaard, 2013) revealed that only 18 %

of the farmers were willing to spend more than 3 minutes extra per field for e.g. typing in infor-

mation. However, at least two third of the respondents also found that VRA of pesticides and ferti-

lisers were important.

In spring 2016 a survey was sent to Danish farmers who had applied for subsidy to invest in a

sensor guided sprayer or had previously bought a Yara N-sensor (see appendix 1). The study

indicates that farmers who had invested in a crop sensor have a high level of knowledge on how

the sensor adjusts the application rate for plant protection. 33 % did not know whether or not to

apply more or less fertiliser where plant coverage is low. However, most algorithms actually re-

duce the dosage when plant coverage is below a certain value, whereas other algorithms have an

inverse linear relationship between NDVI and application rates, therefore the result may reflect

the different algorithms rather than lack of knowledge. Of the responding 23 farmers, 78 % had

invested in a Yara N-sensor and 55 % had invested in the crop sensor with subsidy from the envi-

ronmental technology scheme. Despite this investment, 43 % did not use the sensor for VRA of

nitrogen and 38 % did not use it for spraying. This low usage may be explained by several fac-

tors: 52 % of the responding farmers had not experienced a reduction in the use of fertiliser and

pesticides; to the statement “my sensor is simple to use” only 34 % agreed, implying that the

complexity may be a barrier; furthermore, 52 % had experienced technical problems.

The Danish survey (appendix 1) may also provide some guidance as to where farmers get advi-

sory support as 50 % used the support provided by the sensor manufacturer or dealer, whereas

only 25 % had used their local crop management consultant. Only 5 % of the farmers perceived

the support to be highly adequate.

Recent interviews with 11 Danish stakeholders (farmers, industry, and advisory system) in preci-

sion agriculture reveal that the majority of the interviewed stakeholders experience high interest

for PA technologies among farmers, and they all expected that PA technologies will be more inte-

grated in future crop production as a result of the general digitisation of society. The interviewed

stakeholders mainly expect savings in pesticides and fuel consumption and on large farms they

think there may be considerable savings on other resources such as fertiliser use. However, there

seems to be a general agreement among the interviewed stakeholders, that the technologies are

used too little, even when the technology is present at the farm. The interviewed farmers used

auto-steering, sensor, yield sensor, controlled traffic farming and software products for planning

and registration. The group of Danish farmers working actively with PA technologies are estimat-

ed to be 2-10 % of the total number of Danish farmers; however, area-wise they constitute a larg-

er part of the Danish farmland (Thierry et al., 2016).

14 / 33

Among the possible barriers for the use of technology, approx. half of the interviewed mentions

the economic situation for agriculture, whereas others perceive investments in technology as a

necessity in order to produce a profit. The interviewed advisors experienced lack of user friendli-

ness and compatibility between different brands of equipment as a barrier, whereas some of the

companies pointed towards more education and a better data infrastructure on the farms as the

way forward to mainstream PA technologies (Thierry et al, 2016).

Conclusions regarding barriers for use of crop sensors

The sensor technology is adopted primarily by farmers with a general interest in technology,

and these farmers will typically have adopted GPS, yield monitoring several years ago and

use e.g. biomass/yield maps actively in the management.

Yield increases from VRA compared to uniform application of fertiliser or pesticides are small

Farmers often experience lack of user-friendliness in new technology

Farmer experience technical problems and lack of compatibility in technology between brands

Farmers are stressed for time, and are unwilling to spend more than 3 minutes extra per field

task to get e.g. new technology started

Farmers often feel that they receive inadequate technical and agronomic support

Farmers do not use their local advisor for support within this area

Can we identify best practice of crop sensor usage?

One of the farmer journals described an example where a farmer, who had successfully adopted

the technology for both fertilisation and spraying, estimated that the investment had paid for itself

within 4 years. This indicates that documentation of usefulness and benefits for the farmer,

which includes the added value of multiple uses at the farm level, is essential. Secondly the farm-

ers who are not as interested in technology would benefit from learning how to get started,

computer settings etc. from other more technology interested farmers. To narrow the ac-

ceptable timespan for solving technical problems, a high level of advisory support and/or quick

fix easily comprehensible guidelines may enable the farmer to keep using the technology. Fi-

nally it seems that the less technology interested farmers should be targeted with a more “plug-

and-play” approach.

15 / 33

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Appendix 1. Danish survey on current use of crop sensors and barriers for use

Abstract

In connection to the project Putting Sensors to Work – Targeted application of nutrients and pes-ticides (Targ_App) a questionnaire was designed to supplement literature reviews and shed light on the key issues that needs to be addressed in order to mainstream sensor based management. The aim was to investigate the current use as well as barriers for effective implementation of crop sensors in agriculture. Farmers who applied for funding under the Danish subsidy scheme for green technologies were asked about their use of crop sensors, about their perception of crop sensor effectiveness in regard to fertiliser, plant protection and yield and their expectations in re-gard to advisory services. 213 farmers applied for funding and around 35 were contacted, 19 farmers replied and additionally four farmers replied partly. 8 Yara costumers received the ques-tionnaire.

Introduction

One of the critical challenges for the successful and widespread adoption of Precision Farming (PF) in Europe is to mainstream the use of the technologies to ensure it is accessible to all farm-ers and can become an integral part of crop management supported by crop advisors / agrono-mists where decisions are based on cost-benefit analysis. The overall aim of the project Putting sensors to work – Targeted application of nutrients and pesticides (Targ_App) is to achieve ap-propriate research-based validation and comprehensive support to advisors and farmers. This is identified as essential to facilitate the adoption of crop-sensor based management. This question-naire contributes to a clarification of the reality faced by farmers using crop sensors and thereby to shed light on the key issues that needs to be addressed in order to mainstream sensor based management.

Method

A questionnaire was designed with assistance from three specialist advisers as well as one local adviser all engaged with projects and practical advisory services concerning PF. The question-naire was sent to 35 farmers, using the service SurveyXact1, and asking them to share their knowledge on and experience with crop sensors. The respondents were asked 29 questions, be-sides that they were asked for further elaborations. The first question was whether they have crop sensors on their farm or not. Depending on the answer they were redirected to further questions. If the answer was no, the respondent were asked eight more questions. If the answer was yes, the respondent was redirected to 28 further questions. Some gave up personal data e.g. age, name and telephone number for further interview. Most of the farmers receiving the questionnaire were contacted due to their application for funding under the Danish subsidy scheme for technol-ogy. The Subsidy scheme is funding investment in green technologies e.g. crop sensors and the recipients were thus likely to own crop sensors or at least to have considered investing. A few of the farmers who received the questionnaire are Yara customers who have purchased before the subsidy schemes were initiated in 2012.

Results

213 farmers, considering investing in crop sensors, applied for funding under the Danish subsidy scheme. Therefore they were relevant recipients. However, contact information was obtainable for only 35 farmers, 19 replied and 4 replied partly. Thus, the response rate was 66 %.

1 Ramböll, http://www.surveyxact.dk/

19 / 33

The first question separated respondents in two groups, those who already invested in crop sen-sors and those who did not. Only one of the respondents did not own a crop sensor. The remain-ing 22 respondents were redirected to further 27 questions.

Question 1. Do you have crop sensors on you farm? Yes/no.

Question 2. The respondents were asked which manufacturer they purchased their crop sensor

from.

Question 3: How did you finance your crop sensor? Own financial contribu-

tion/subsidy/combination.

20 / 33

Question 4: When did you purchase your crop sensor?

Question 5: To what extent do you agree to the following?-I use crop sensors for nitrogen applica-

tion. Strongly agree/agree/disagree/Strongly disagree/don’t know.

21 / 33

Question 6: To what extent do you agree to the following?-I use crop sensors for application of

plant protection. Strongly agree/agree/disagree/Strongly disagree/don’t know.

Question 7: To what extent do you agree to the following?-The use of crop sensors has lowered

the chemical consumption. Strongly agree/agree/disagree/Strongly disagree/don’t know.

Question 8: To what extent do you agree to the following?-The use of crop sensors has lowered

the fertiliser consumption. Strongly agree/agree/disagree/Strongly disagree/don’t know.

22 / 33

Question 9: To what extent do you agree to the following?-My crop sensor is simple to use.

Strongly agree/agree/disagree/Strongly disagree/don’t know.

Table 1: Further comments to question nine.

Comment Kommentarer

We want data from CropSAT and will use these

data. Manual evaluation of each field based on

our own yield data are taken into account.

Vi går efter data fra CropSAT og vil bruge disse

data. Efter manuel gennemgang af hver enkelt

mark og vor egne høst data tages i betragtning.

As long as we have so little fertiliser and we are

forced to under-fertilise, then it cannot be used

for anything.

Så længe vi har så lidt gødning at gøre med og

vi er tvunget til at undergøde hele vejen rundt,

ja så kan det ikke bruges til noget.

Generally there has been too little information

from the dealer.

Der har generelt været alt for lidt information fra

forhandleren.

I have stopped using it - does not seem to get

enough out of it.

In addition, as it was to be upgraded I needed

to buy me a new one, full price.

Jeg er holdt op med at bruge den - synes ikke

at få nok ud af den.

Derudover da den skulle opgraderes skulle jeg

anskaffe mig en ny til fuld pris

Question 10: To what extent do you recognize the following challenge when using crop sensors?

- missing effect on yields. Strongly agree/agree/disagree/Strongly disagree/don’t know.

23 / 33

Question 11: To what extent do you recognize the following challenge when using crop sensors?

– Technical issues during start-up. Strongly agree/agree/disagree/Strongly disagree/don’t know.

Table 2: Further comments to question 11.

Comments Kommentarer

I haven’t used time making it work, it’s not rele-

vant with the under-fertilisation we have on our

dairy farm.

Har slet ikke brugt tid på at få det til at virke,

ikke relevant med den undergødskning vi har

på vores bedrift med køer.

Too often we lost our signal. Mistede for til signal.

Question 12: Your knowledge on crop sensors – Areas dominated by a dense biomass is given

more nitrogen compared to areas with less dense biomass. Agree/disagree/don’t know.

24 / 33

Question 13: Your knowledge on crop sensors – dose is increased in areas with dense biomass

when targeting fungal attacks. Agree/disagree/don’t know.

Question 14: Your knowledge on crop sensors – dose is increased in areas with dense biomass

when using growth regulators. Agree/disagree/don’t know.

Question 15: Your knowledge on crop sensors – For desiccation, dose is increased in areas with

thin biomass coverage. Agree/disagree/don’t know.

25 / 33

Question 16: Your knowledge on crop sensors – When targeting weed in winter cereals, dose is

increased in areas with thin biomass coverage. Agree/disagree/don’t know.

Table 3: Further comments to question 16.

Comments Kommentarer

That’s what you need to tell us, get going. Det er jo det som i skal fortælle os, kom i gang.

In the first question a growth stage is missing. 1. Spørgsmål mangler et vækst-stadie.

Question 17: To what extent do you agree in the following statements concerning support in re-

gard to the use of crop sensors? – I used advisory services from the dealer during start-up.

Strongly agree/agree/disagree/Strongly disagree/don’t know.

26 / 33

Question 18: To what extent do you agree in the following statements concerning support in re-

gard to the use of crop sensors? I used advisory services from my local advisor during start-up.

Strongly agree/agree/disagree/Strongly disagree/don’t know.

Question 19: To what extent do you agree in the following statements concerning support in re-

gard to the use of crop sensors? The advisory services available were sufficient. Strongly

agree/agree/disagree/Strongly disagree/don’t know.

27 / 33

Question 20-22.

Question 20: What did you need

support for?

Question 21: What kind of

support could you have want-

ed from the dealer?

Question 22: What kind

of support could you

have wanted from the

local adviser?

We need

web-based

software

with a holis-

tic approach

that can

create allo-

cation maps.

(soil quality

cards, yield

maps, soil

analysis,

and not least

the operator

experience)

Vi mangler webba-

seret software, som

ud fra en holistisk

indgang, kan lave

tildelingskort. (boni-

tetskort; udbytte-

kort; jordbundsana-

lyser; og ikke

mindst driftslederer-

faringer)

A product

that works

Et produkt som

virker

Some expe-

rience from

other farm-

ers who had

used similar

equipment

Nogle erfaring ved

planteavler som

havde brugt lignen-

de udstyr

Experience Erfaringer Experience Erfaringer

Start-up,

exchange of

experience

Opstart, erfarings-

udveksling

Better start-

up/settings

Bedre i gang

sætning/indstilling

None Ingen

28 / 33

Things are

moving so

fast that

software are

not devel-

oped before

new hard-

ware are

developed.

We have

seen that

the equip-

ment not be

fulfilled the

expectations

that we had.

Udviklingen går så

hurtigt at software

ikke når at blive

udviklet, inden der

er udviklet nye

hardware. Vi har

oplevet at udstyret

ikke har kunnet ind-

fri de forventninger

som vi havde.

(The need

for support

red.) It was

sparingly. I

asked the

network of

practitioners,

and used

their own

experiences

Det var sparsomt.

Jeg spurgte i net-

værket af praktike-

re, og brugte af eg-

ne erfaringer.

The dealer

must deliver

the chosen

product,

and take

care of the

start-up.

Except that

I mostly

stick to

practical

experience.

Forhandleren

skal levere valgt

produkt, sørge for

igangsætning.

Ellers holder jeg

mig mest til prak-

tiske erfaringer.

Results

from field

practice

and less

results

based on

research in

plots

Tal fra

mark prak-

sis og min-

dre tal fra

forsøgs

parceller

That they

can show

where you

can get re-

sults from

using it,

where you

can earn

money that

is.

At de kan vise hvor

man kan få noget

ud af at bruge det,

altså hvor man kan

tjene noget.

He should

just tell how

it works and

how to in-

stall it.

Han skal bare

fortælle hvordan

det virker og

hvordan det skal

indstilles.

How to

make

money on

it.

Hvor man

tjener pen-

ge på det.

Setting up

the equip-

ment. Info

regarding

functions

etc.

Opsætning af ud-

styr. Info vedr. funk-

tioner etc.

Technical

support

teknisk support Pointing

out rele-

vant tech-

nology to

be used

hvilken

relevant

teknologi

der bør

anvendes

29 / 33

Correct set-

up and use

of equip-

ment.

Rigtig opsætning,

og brugen af udsty-

ret.

Someone

who knew

more about

our needs.

En der vidste

mere om vores

behov.

More sup-

port for

what the

equipment

can be

used for.

Mere hjælp

til hvad

udstyret

kunne bru-

ges til.

To apply for

the subsidy

Søge støtten That their

knowledge

was updat-

ed, and that

they

showed us

what could

be done in

practice.

We experi-

enced that

everything

could be

done until

we bought

the equip-

ment.

At deres viden

var opdateret, og

viste hvad der

kunnet lade sig

gøre i praksis. Vi

har oplevet alt

kunne lade sig

gøre indtil vi køb-

te udstyret.

I have dif-

ficulties

seeing

what they

could con-

tribute

with.

Har svært

at se hvad

de vil kun-

ne byde

ind med.

Overall start-

up

Overordnet opsæt-

ning

Course Kursus Overall

use

Overordnet

brug

Make it work Få det at virke A start-up

package

En opstarts pak-

ke.

How the

variable

rata appli-

cation are

best uti-

lised.

Hvordan

den gradu-

erede tilde-

ling udnyt-

tes bedst.

Start-up Opstart Course Kursus None ingen

Adjusting

the sensor

to make

regulate as

much as

possible

Justering af sensor

for at få den til at

regulere så meget

som muligt

30 / 33

Technical

issues and

the differ-

ence be-

tween the

yield- and

protein ferti-

lisation.

Det tekniske samt

forskellen mellem

udbytte - og prote-

ingødskning.

Everything

was okay

Alt ok When I

started

many

years ago

the specific

knowledge

about the

use of

sensors

was miss-

ing.

Da jeg

startede for

mange år

siden

manglede

der konkret

viden om

brugen af

sensoren.

Start-up Opstart What I got Det jeg fik they do not

know the

technique

of each

sensor

de kender

jo ikke tek-

nikken i

den enkel-

te sensor

Annual up-

grade-

course or-

ganized by

Yara

Der var årlig opgra-

dering kursus af-

holdt af Yara

Running-in Indkøring Support for

start-up

Hjælp i opstart None ingen

Question 23: Which data do you utilise on your farm? Yield maps, weed maps, N-application

maps, lime application maps, auto-steering, variable seeding, field boarders, if others, which?

31 / 33

Table 4: Further comments to question 23.

Comment Kommentarer

Experience Erfaringer

Question 24: Which data do you save from your farm? Yield maps, weed maps, N-application

maps, lime application maps, auto-steering, variable seeding, field boarders, if others, which?

Comments Kommentarer

Stones, wells etc. Sten; brønde etc.

Question 25: To what extent do you agree to the following? – I expect that the use of data on my

farm will increase in the future. Strongly agree/agree/disagree/Strongly disagree/don’t know.

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Question 26: To what extent do you agree to the following? – I expect that I will be collecting

more data from my farm in the future. Strongly agree/agree/disagree/Strongly disagree/don’t

know.

Comments Kommentarer

I am the new manager and it (collecting data

red.) will be amongst my focus areas

I forbindelse med at jeg er ny driftsleder på

stedet vil det være blandt mine fokusområder

If the technique is ok then it will be used more. Hvis teknik ok mere brug.

Discussion

The questionnaire was sent to 35 out of 213 farmers applying for funding via the Danish subsidy scheme for green technologies. 23 from the 35 replied thus giving a response rate equal to 66 %. However, only 11% of the potential owners of crop sensors have replied. The 213 applicants have not necessarily invested in crop sensors and thus the number of 213 farmers does not reflect the actual number of crop sensors purchased in Denmark. The relatively small number of respond-ents should be taken into account when analysing the results. To the question: “do you use a crop sensor when applying fertiliser and pesticide?” only 5 percent answers “strongly agree” in regard to fertiliser and 0 percent when it comes to plant protection. 43 and 38 percent replies “strongly disagree” to the use of crop sensors when applying fertiliser and plant protection, respectively. When asked whether crop sensors have reduced the overall use of plant protection, 52% an-swers “strongly disagree” for both plant protection and fertiliser and 29 and 38 percent replies “disagree”. To the question whether the use of crop sensors have an effect on yield the picture is unclear (see Question 10) with 14, 14, 38, 14 percent replying “strongly agree”, “agree”, “disa-gree” and “strongly disagree”, respectively. This may partly be explained by the relatively small number of respondents, which weakens the strength of the analysis. To the questions “do you find crop sensors easy to use?” and “did you experience technical prob-lems during start-up?” (Question 9 and 11) the answers are relatively evenly distributed across strongly agree/agree and strongly disagree/disagree. This pattern implies a very heterogenous user-group. The heterogeneity of the group of farmers using or owning crop sensors are also ex-posed in question 20 – 22. Despite that the questions shed light on the expectations towards ad-visors, the three questions also reveal that some farmers are very specific in their need for sup-port, some are very unspecific and others have some kind of frustration in relation to bad experi-ences with the technologies. One demands for a specific software solution and uses the term “ho-listic” to underpin the necessity of a wide perspective in decision support tools. Another just want

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it to work not implying an interest in the technology. Finally, one experienced that the dealer promised more than he could deliver when the respondent purchased the equipment. In Question 11 a farmer points out a problem with poor internet connection in the field. This prob-lem has, in other more extensive questionnaires, been identified as a barrier for implementation of PF-technologies in general (Bligaard, 2012). The role of dealers and advisers are addressed in six questions (Question 17-22). Only 5% of the respondents answered “strongly agree” or “agree” to whether they used their local advisor during start-up. Whereas 50% answered “strongly agree” or “agree” to whether they used support from the dealer during start-up. This indicates that the role of the local advisor from a farmer’s point of view is less important when it comes to implementing crop sensors. However, independent sup-port might reduce some of the investments that have resulted in frustration and lost investments. One of the important findings in the EIP-AGRI Focus Group report on precision farming is the need for well-trained advisors in the field of precision agriculture. Further, several of the com-ments in Question 22 implies, crop sensor technology is not an area of expertise farmers connect to advisors.

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