Systematic planning and cultivation of agricultural fields using a geo-spatial arable field optimiza

Special Issue:Operations Management Research Paper

Systematic planning and cultivation of agricultural ?elds using a geo-spatial arable ?eld optimization service:Opportunities and obstacles

Sytze de Bruin a ,*,Peter Lerink b ,Inge https://www.360docs.net/doc/b217980438.html, Riviere c ,Bas Vanmeulebrouk c

Laboratory of Geo-Information Science and Remote Sensing,Wageningen University,PO Box 47,6700AA Wageningen,The Netherlands b

IB-Lerink,Laan van Moerkerken 85,3271AJ Mijnsheerenland,The Netherlands c

Alterra,Wageningen-UR,PO Box 47,6700AA Wageningen,The Netherlands

a r t i c l e i n f o

Article history:

Received 28December 2012Received in revised form 12June 2013

Accepted 23July 2013

Published online 22August 2013

This paper describes a geo-spatial arable ?eld optimization service (GAOS)and an assessment of users’experiences after three years of experimental operation.The service was developed in close cooperation with farmers.It allows farmers to optimize the locations of tracks within their ?elds,explore different options and download these to commercial Global Navigation Satellite System-guided steering systems.GAOS runs as a web service using standards de?ned by the Open Geospatial Consortium and is being operated by a group of farmers who received a few hours of training.The objective of optimization is to maximize ef?ciency by avoiding both inef?cient turns and discontinuous swaths.Where applicable,released space is converted into ?eld margins that meet environmental objectives and potentially generate additional income.The system provides dedicated functionality for geometrical operations,such as uploading and splitting of ?elds,merging and splitting of ?eld edges,and manual editing of reference lines in the headlands.Acknowledged bene?cial features include reduced expenditures on time and wasted resources and support for planning spraying paths.Given its complexity,most farmers preferred specialists to operate the system rather than oper-ating it themselves.Future development should aim for simpler operation and full support for interactive coverage planning as well as computational optimization.

1.Introduction

Field margins in agricultural landscapes have multiple func-tions.These include reducing diffuse pollution in lowland areas (Borin,Passoni,Thiene,&Tempesta,2010),providing habitats for species of potential agronomic value (Haenke,Scheid,Schaefer,Tscharntke,&Thies,2009;Holland,Oaten,

Southway,&Moreby,2008;Olson &Wa ¨ckers,2007)and serving as foraging areas for farmland birds (Douglas,Vickery,&Benton,2009).Field margins can also help to restore con-nectivity in fragmented ecological networks (von Haaren &Reich,2006).Moreover,European landscapes are increas-ingly being perceived as leisure commodities (Buijs,Pedroli,&Luginbu ¨hl,2006)and species-rich and ?owering ?eld margins

*Corresponding author .Tel.:t31317481830.

E-mail addresses:sytze.debruin@wur.nl ,sytbru@https://www.360docs.net/doc/b217980438.html, (S.de

Bruin).

Available online at

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have been found to signi?cantly contribute to the appreciation of agricultural landscapes(Junge,Jacot,Bosshard,& Lindemann-Matthies,2009;Stilma et al.,2009).Recent pro-posals for the EU Common Agricultural Policy2014e2020 contain provisions for farmers to allocate up to7%of their land as ecological focus areas,which include?eld margins (Allen,Buckwell,Baldock,&Menadue,2012).

Establishing corridors along?eld margins between patchy ecological communities and visually enhancing the landscape can be supported by methods for optimizing the spatial con?guration of hedgerows.Groot et al.(2007)presented a tool (IMAGES)for generating alternative spatial con?gurations of hedgerows,which later was demonstrated in an area of grassland covering873ha(Groot,Jellema,&Rossing,2010). Similar methods could be used to support local or regional planning of?eld margins in intensive arable landscapes.As planning tools such as IMAGES use spatially aggregated in-dicators,they can facilitate the design of alternative land-scape development options for areas covering several farms, but they barely address the local spatial preferences of farmers who are to implement the plans at?eld level.In intensive agricultural regions,such individual preferences are important considerations,as illustrated by a study in Ger-many in which farmers preferred to adapt the location of?eld margins to their speci?c needs(Mante&Gerowitt,2009). Moreover,according to a study by de Snoo(1999),the most important condition for accepting the incorporation of un-sprayed?eld margins into arable farming practices was that the widths of the?eld margins can vary.These results high-light the need to adapt?eld margins to local circumstances, which is consistent with de Bruin,Lerink,Klompe,van der Wal,and Heijting(2009),who showed that there are poten-tial operational bene?ts in areas of inef?cient machine manoeuvring if the widths of?eld margins are optimized, which typically involved varying the widths of the?eld margins.

Accordingly,farmers may be helped by a tool that allows them to adapt regional plans for establishing?eld margin strips to their own situation and to assess their plans prior to actual implementation.Additional opportunities and chal-lenges emerge if optimized spatial?eld plans can be used for automated vehicle navigation and controlling the rate of application of fertilizers or agrochemicals within the cropped area.For example,precision guidance along prede?ned?eld tracks in combination with auto-boom control of a sprayer has been demonstrated to lead to substantial savings in travelling distance and the amounts of inputs used(Batte& Ehsani,2006;Palmer,Wild,&Runtz,2003),with bene?ts to both the farmer and the environment.

Coverage planning and related methodologies have received considerable academic interest in recent years (Bochtis&S?rensen,2009;Choset,2001;Hameed,Bochtis, S?rensen,&N?remark,2010;Jin&Tang,2006;Oksanen& Visala,2009;Spekken&de Bruin,2013;Ta?¨x,Soue`res, Frayssinet,&Cordesses,2006),but little progress has been made towards extensive operational https://www.360docs.net/doc/b217980438.html,mercial systems allow farmers to create a pattern of swaths by?rst de?ning a reference track(AB line)with their tractor and then selecting from a set of prede?ned patterns.However,in this method it is very dif?cult to know in advance whether the ?nal swath will be properly aligned with the opposite?eld boundary.Besides,the optimization of agricultural operations in combination with?eld margins may require a different approach than conventional coverage planning.While the latter aims to optimize paths in such a way that the?eld is entirely covered by the primary crop,combined planning of crop and?eld margins implies that strips of yet unknown and potentially variable width(within limits)have to be created along designated edges of the?eld.

de Bruin et al.(2009)proposed a general approach for the spatial optimization of straight cropped swaths and?eld margins using geographical information technology.This method was further developed and tested in close interaction with a group of30innovative farmers and early adopters of technology in the Hoeksche Waard,a national landscape south of Rotterdam in the Netherlands.In this polder land-scape,a dense network of?eld margins is being developed to connect several relatively widely-spaced dikes and creeks. This?eld testing led to the development of a prototype geo-web service called GAOS(Geo-spatial Arable?eld Optimiza-tion Service)that can be operated by non-GIS(Geographical Information System)experts from an ordinary web browser. Tests with GAOS covered the entire pathway from initial acquisition of accurate?eld geometry(de Bruin,Heuvelink,& Brown,2008)to the management of cropped swaths and?eld margins using real time kinematic(RTK)global navigation satellite system(GNSS)guidance systems on the farms.This allowed us to identify factors affecting the acceptance of an automation tool for systematic planning and cultivation of agricultural?elds.

The purpose of this paper is:(1)to present an update of the methodology and technology of GAOS,(2)to qualitatively assess the experiences of farmers in relation to?tness-for-purpose and acceptance of such a tool,and(3)to suggest di-rections for further development.

2.Materials and methods

2.1.Description of the geo-spatial arable?eld optimization service

2.1.1.Operational objectives

GAOS emerged from a set of practical challenges in the Hoeksche Waard,including the creation of a network of paths and?eld margins using commercially available auto-steering equipment.The total area of the Hoeksche Waard is approxi-mately27,500ha.So far the geometry of360?elds has been accurately measured;these?elds,which cover3858ha, constitute our dataset.The speci?c context and problems in the area resulted in a set of design choices that may be un-common and possibly impracticable in other parts of the world.For example,since optimized routing(Bochtis& Vougioukas,2008;Spekken&de Bruin,2013)was not sup-ported by the auto-steering equipment operated by farmers in the study area,GAOS does not currently compute an optimized order of swaths.Other design choices are explained below.

In general,?elds in the Hoeksche Waard can be broken down into a main cultivated area e also known as the‘block of the?eld’(Hameed et al.,2010)e zero or more cropped

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headlands where machinery is turned,?eld margins,and buffer strips along watercourses.Although the geometry of most agricultural?elds in the Hoeksche Waard is quite simple compared with some of the?elds shown in Hameed et al. (2010),many?elds in the dataset have more than four sides and in places are indented.The headlands of optimized?elds in our database occupy on average20%of the cultivated area. Field margins are grown with mixtures of grasses or grasses and wild?owers,for which subsidies are available where the ?eld margins exceed a given width.Most of the block of the ?eld is divided into adjacent straight swaths approximately parallel(within5 )to one of the edges of the?eld.This is feasible in our set of?elds but it will not work in?elds having curved boundaries all around,such as circular?elds.Any spaces between the outermost swaths and the nearest?eld edges either serve as non-cropped buffer strips or are con-verted into?eld margins.These may vary in width since?eld edges are almost never perfectly straight lines,while opposite edges may not be exactly parallel.Where there are no?eld margins,compulsory buffer strips have to be established along watercourses(cross-compliance rule of the European Union).At some locations,machinery can be turned outside the?eld or on wide buffer strips or?eld margins jutting out from the swaths in the main cultivated area.In such cases there is no need for a headland along the corresponding?eld edge.

In the Hoeksche Waard it is common practice to align spray paths with the centre line of regular swaths.Accordingly, spray paths are a subset of the regular tracks.Sections of the spray boom can be shut off or folded to make the spray swaths narrower where the?eld geometry makes this necessary.

2.1.2.Spatial planning support

GAOS requires accurate?eld geometry(de Bruin et al.,2008), which was acquired using an RTK-GNSS receiver mounted on a quad.Currently,the system employs two dimensional ge-ometry and assumes that there are no major height differ-ences in the?eld that need to be taken into account for navigation and planning purposes.Given the?eld geometry, the swath width and the farmer’s preferences regarding the locations and the minimum and maximum widths of?eld margins and the minimum width of compulsory buffer strips, GAOS attempts to optimize the direction and positional shift of the swath pattern in the main cultivated area.The objective function to be maximized is the balance between the loss of area of the primary crop and losses due to turning versus any subsidy received for?eld margins.The system?rst derives a set of driving angles parallel and perpendicular to the orien-tations of the edges of the?eld and a sequence of incremental shifts which fully cover the swath width in narrow(e.g.0.1m) intervals.The operator of the service can exclude certain edges from the above calculations to limit the search space or impose a preferred direction for navigation.Although there is no guarantee that the tested angles will include the optimum direction for?elds of arbitrary shape,the heuristic has proven useful for?eld shapes encountered in the Hoeksche Waard. The set of driving angles and incremental shifts is then exhaustively tested to?nd the combination that maximizes the objective function.The corresponding swaths,paths,pri-mary reference lines(used for navigation in the main cultivated area)and?eld margins are stored in a database.For a more detailed description of the optimization method,see de Bruin et al.(2009).

After optimizing the driving angle and the shift,headlands are added interactively by the operator of the service.Manual operation of the headland tool was chosen because the30 participating farmers wanted headlands with different widths and curvatures(if any).To add headlands,the operator selects an edge of the?eld and,if needed,adjusts the default number of swaths and the distance between the?rst swath and the edge.In contrast to the pattern on the main cultivated area, the secondary reference lines of the headlands may be curvilinear and curves can be straightened or smoothed using a splines tool.Note that reduced yields on the headlands are accounted for in the costs of turning used in the objective function for optimizing the main pattern.

The system also computes the gross area of the?eld,the cropped area,the area occupied by?eld margins and other non-cropped areas.The geometry of secondary reference lines can be manually adjusted using editing tools.Any addition or modi?cation of secondary reference lines is fol-lowed by a re-computation and storage of geometries and surface areas,and the results are almost instantaneously visible to the operator.The database allows storage of multi-ple versions of plans,which facilitates comparison of alter-native solutions.

Presently,GAOS has no functionality for automatic avoid-ance of obstacles in the?eld.However,obstacles,such as electricity pylons,are stored in the database and shown on the screen so the operator can manually overrule spray paths, which by default are assigned to the centre of the full width of the sprayer,to circumvent the obstacle.The farmer can retract or fold sections of the sprayer boom as necessary. Computed reference lines can be downloaded and transferred to auto-steering equipment.GAOS produces exchange?les for two brands of auto-steering equipment most commonly used in the Hoeksche Waard(Trimble and SBG Precision Farming). The system runs as a web service and it has been used mainly by three farmers,who acted as a Geo-Information Service Point(GISP)for another27farmers in the Hoeksche Waard. Figure1is a sequence diagram of the work?ow and the data?ow.

2.1.

3.System architecture

GAOS is implemented in a service-oriented architecture(SOA).

A SOA separates functions into distinct services that are accessible over a network and can be combined and reused in web applications.These services communicate with each other by exchanging data in a well-de?ned,shared format,or by coordinating activities between two or more services.There were two reasons for applying SOA principles in the GAOS project.First,it allows users of the GAOS system to run the system on any personal computer(PC)with internet access and a web browser,without having to install potentially complex GIS software and a spatially enabled database.Second,it fa-cilitates dissemination of updates of the software,which was deemed necessary given the iterative nature of the project.

GAOS employs a typical three-tier architecture in which data,logic and presentation are in separate layers,as shown in Fig.2.The data layer consists of a spatially enabled

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relational database management system (The PostgreSQL Global Development Group,2013)containing the datasets needed by GAOS.Spatial data are stored using the PostGIS extension (PostGIS,2013).The logic is made available in the service layer,which is described below.The presentation layer is crucial,since it is the interface with the end user and presents the information that is needed.The presentation layer consists of an internet GIS client created using LuigiOS,an open source internet GIS client framework for the Flex platform (LuigiOS,2010).

The Open Geospatial Consortium (OGC)has produced a series of standards for implementing web services for spatial data.The following speci?cations are used in GAOS:web map service (WMS),web feature service (WFS)and web processing service (WPS).A WMS is used for data visualization,dynami-cally producing maps that portray geographical information as digital image ?les suitable for display on a computer screen (Open Geospatial Consortium Inc.,2004).The WMS in GAOS is used to display a topographical map for orientation purposes.Implementation was based on the open source internet GIS server GeoServer (GeoServer,2013).

A WFS provides a client with access to geospatial features encoded in geography markup language (GML).GAOS uses a transactional WFS (WFS-T,Open Geospatial Consortium Inc.,2005)allowing the internet GIS client to both retrieve and update geospatial data.The internet GIS client renders the feature data on top of the topographic reference map.The user can edit the feature data using tools provided by the internet GIS client.Subsequent WFS-T requests store the edited data on the server.Implementation of the WFS-T ser-vices was also based on GeoServer.

The optimization and related spatial operations (Section 2.1.2)were implemented as WPS processes using PyWPS (PyWPS,2013).PyWPS is a Python version of the OGC WPS implementation speci?cation,which de?nes a standardized interface that facilitates publishing,discovery and binding of geospatial processes (Open Geospatial Consortium Inc.,2007).The Python scripts used for the spatial operations of GAOS rely heavily on the Geospatial library OGR (GDAL/OGR,2013).

2.2.Assessment 2.2.1.

Claims and opinions

GAOS aims to help farmers plan the layout of cropped swaths and ?eld margins and facilitate realization of their plans by supporting communication with off-the-shelf auto-steering solutions.The ?rst claim inferred from this objective is that farmers are indeed helped by the tool.In the absence of

Fig.1e Sequence diagram showing actors,work?ow and data?ow related to the use of GAOS.GISP is the geo-information service point,i.e.three farmers who operated the system and acted as agents for the other participants in the

project.

Fig.2e GAOS software stack showing the layered architecture.

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independent comparison and quantitative measurements, the claim was assumed to be valid if the tool was perceived to be useful for:

allocating and dimensioning?eld margins;

planning spray paths;

reducing expenditure on time and wasted resources;

avoiding structural damage to the soil.

The latter two criteria were considered to be implied by avoiding inef?cient?eld operations.The second claim infer-red from the objective is that GAOS facilitated communication with auto-steering equipment.This would be supported by endorsement of improved communication with third parties, including contractors using such devices.

Apart from an assessment of the usefulness of GAOS,we were also interested in the farmers’preferences for:

automated planning using an objective function versus manual digitizing and adjustment of reference lines based on expert judgement;

operation of a service by a specialist versus operation by individual farmers themselves.

Finally,we wanted to know farmers’opinions on the de-mand for a planning tool such as GAOS and the acceptance and perceived ease of use of the offered solution.

2.2.2.Questionnaire and interviews

Starting in2009,three versions of GAOS have been developed in close cooperation with farmers.The third version was evaluated using a questionnaire sent to all30participants involved in the latest round of development and testing.The questionnaire consisted of15propositions,nine about the perceived usefulness of the tool and six broader opinions (details are given in the results section),to be scored on a scale of1e5:strongly disagree,disagree,neutral,agree,strongly agree.The participants were also asked whether they currently used auto-steering.In addition,?ve in-depth in-terviews were conducted to obtain more detailed information,?nd explanations for the results of the questionnaire and assess the perceived ease of use of the tool.These interviews consisted of29open questions;English translations of the questions are provided in Table1.

The interviewees(who also?lled in the questionnaire) were the three members of the GISP team,the initiator of several innovation projects in the Hoeksche Waard and a contractor/farmer who has been involved in measuring?eld geometry since the early days of GAOS.

3.Results and discussion

3.1.Example spatial optimization

Figure3shows a screen dump of the GAOS internet GIS client with an optimized pattern of swath centres and spray paths on the main cultivated area and manually added headlands for a?eld measuring19.2ha.The GISP team used screen shots like Fig.3or.kml exports for Google Earth(see export button)when communicating with the other farmers.Figure4out-lines the division of the same?eld into the main spatial units. The inset of Fig.4shows how the manual addition of head-lands caused void areas between the extremes of swaths and the inner edge of the headland.Before adding the headland, wasted areas of virtually the same size were located along the ?eld boundary.

The relative revenues for the tested angles e derived from the geometry of the?eld e and incremental shift are shown in Fig.5.To improve legibility only?ve curves are shown.

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The lowest relative revenue amounted to 96.2%,which means that the maximum bene?t from optimization approached 4%.Of course,such values depend on the ?eld characteristics and the costs considered in the computations,which in this case were set as described in de Bruin et al.(2009):a subsidy of V 0.1667m à2for ?eld margins,a cost of V 0.2023m à2for area loss and a cost of V 7.285per additional turn for a 3m wide swath in a potato crop.The last cost includes a loss of income owing to reduced yields on the headlands.We acknowledge that the computations do not include some potential costs and pro?ts,such as savings on agrochemicals and external-ities such as environmental side effects.Nevertheless,the plot suggests an order of magnitude of savings that can be

achieved by optimizing a swath pattern.Similar results were obtained for other ?elds in the Hoeksche Waard (not shown here).

3.2.Assessment results

Figure 6shows the results from 26returned questionnaires (four participants did not respond).Twenty-three of the 26respondents,including the ?ve interviewed farmers,used RTK-GNSS auto-steering equipment.These ?ve farmers indi-cated that they would again invest in automated navigation tools on their next tractor purchase.Sixteen respondents (62%)agreed that GAOS helped them decide on the size

and

Fig.3e Screen dump of the GAOS client showing a pattern of spray paths (red lines)and swath centres (green lines).The west e east pattern on the main ?eld area was obtained by optimization;the headlands with north e south patterns were added interactively.The ?eld is 19.2ha and in this case just more than 0.7ha would be covered by ?eld margins (as indicated in the lower

pane).

Fig.4e Simpli?ed representation of the ?eld shown in Fig.3showing the main spatial units.The inset shows void areas between the ends of the swaths and the inner edge of a headland.

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locations of ?eld margins (proposition 1)and that the computed areas are useful for management purposes (prop-osition 2).However,?ve farmers disagreed with proposition 2.Twenty farmers (77%)af?rmed that GAOS planning helped them to save time on ?eld operations (proposition 3);the same number of respondents acknowledged support for planning spray paths (proposition 7).The proposition on improved use of workable days (4)received mainly positive feedback (65%),although three farmers disagreed or strongly disagreed (12%).There was signi?cant disagreement with propositions 5,6and 9about saving fuel and agro-chemicals and avoidance of structural damage to the soil (12%,12%and 20%)even though the majority of the respondents were positive (58%,62%and 62%).The ?nal usefulness-related proposition (8)concerning communication with third parties received a majority (54%)of neutral votes.

All respondents expected an increase in the use of auto-steering equipment on arable farms (proposition 12).Eighty-one per cent agreed that there is a demand for a system for planning reference lines (proposition 13)and 69%thought that such a system should be operated by specialists (prop-osition 10),but the respondents differed widely in their desire to operate the system themselves (proposition 11),with a neutral modal score (42%).Sixty-?ve per cent of the re-spondents preferred automatic optimization,but a large majority of the farmers (88%)also indicated that the planning tool should have ample functionality for manual adjustment of plans.

The in-depth interviews provided useful information for interpreting the results of the questionnaire and they also addressed the perceived ease of use of the offered solution.The ?ve interviewees acknowledged that spatial planning of paths and ?eld margins decreases ?eld traf?c,and that reduced overlap helps saving resources.One farmer com-mented that the effect of planned traf?c on soil structure is ambiguous,since ?eld traf?c will intensify on speci?c tracks,leading to compaction and deterioration of soil structure.It was further noted that the above effects cannot be fully attributed to GAOS,since they can also be achieved by RTK-GNSS guided parallel tracking alone.The latter may be an

explanation for the negative responses on propositions 5,6and 9of the questionnaire (Fig.5).

Four of the interviewed farmers preferred to plan paths and ?eld margins in the of?ce rather than on site.The ?fth farmer preferred establishing reference lines in the ?eld with visual reference to the edges of ditches,even though he preferred to plan the spray paths in the of?ce.This farmer had a bad experience with an incorrectly measured RTK-GNSS reference station,as detailed below,which provoked mistrust of the use of reference lines computed on the basis of earlier measurements of ?eld geometry.Similar scepticism among other farmers may have led to the 12%disagreement with proposition 1of the questionnaire.

All ?ve interviewees af?rmed that spatial planning reduces the time needed for ?eld operations (cf.propositions 3and 4).One member of the GISP team complained about the amount of time needed to plan the exercise itself,although quali?ed this by saying that this could be done during the quieter winter months.It was said that reference lines remain valid for several years at least,unless ?eld geometry is changed by the renovation of ditches or the splitting or amalgamation of ?elds.Two interviewees claimed to preserve ?eld margins whatever crop is grown;the others indicated that the size of ?eld margins depends on subsidies received in relation to crop revenues.

The in-depth interviews revealed that the exchange of reference lines was experienced as cumbersome since it required physical transfer by USB (Universal Serial Bus)?ash memory with a critical folder structure and naming con-ventions.This may explain the modal score for proposition 8(Fig.5).One of the interviewees claimed he had been unable to exchange reference lines with a contractor.Instead,he created AB lines in the ?eld using his navigation system (i.e.without GAOS),disk-harrowed the ?rst track so that it could be clearly recognized in the ?eld,and instructed the driver to position the machine in the centre of the marked track and follow a manually entered compass direction.This farmer had been faced with severe positional errors owing to an incorrectly measured RTK base station,which caused posi-tional shifts of as much as 3m.Of course,such errors can be avoided using a certi?ed RTK correction signal.The exchange of reference lines with the John Deere automated guiding system was not possible during our tests because of company policy (avoidance of liability claims).We acknowledge that the communication of reference lines is suffering from teething problems,but the interviewees said these are likely to be resolved in the near future.Considerable effort will be needed to achieve simple and ?awless operation and gain trust in the system.

According to the responses to propositions 14and 15,but contrary to our initial thoughts,the planning system should offer extensive support for interactive manipulation of computationally optimized reference lines.This was unani-mously substantiated during the in-depth interviews.Two interviewees stressed the importance of easy-to-use editing procedures and the presence of an undo button to return to previous results or the original optimization results.One of the GAOS operators claimed that a fully automated procedure would produce usable results only for large ?elds (larger than in the Netherlands);another operator claimed that 80%of the

angle (°)

9596

99100

0.0

0.5

1.0 1.5

2.0 2.5

3.0

r e v e n u e (% o f m a x )

positional shift (m)

1.583.57Fig.5e Relative revenues from the ?eld shown in Figs.3and 4for ?ve directional angles and a series of incremental shifts of 0.1m.

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plans required manual adjustment.The interviewees stressed that farmers need to be more closely involved in the editing sessions than in the tests.This means that either the farmers operate the system themselves or tools are used to support rigorous and prompt interaction between the operator of the system and the farmer requesting a service.The latter could be accomplished by using a screen sharing facility.Whichever option is chosen,the three members of the GISP team

agreed

Fig.6e Responses (%)to the propositions of the questionnaire:1[strongly disagree;2[disagree;3[neutral;4[agree;5[strongly agree.

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that operation of the system should be made more straight-forward and intuitive than is currently the case.

A drawback of the assessed version of GAOS is that the operator is required to understand how the system functions. Together with the experienced complexity of operation,dif-?culties when uploading planning results into the auto-steering controller and e very importantly e a lack of trust in the planning results and the?eld measurements on which they are based,this contributed to a limited degree of uptake of GAOS.The GISP team reported that some farmers were not convinced of the results and tested them in the?eld,which led to a considerable loss of time.They expected?awless operation and dependable representation of the?eld layout, conditions that were not always met by the prototype. Nevertheless,most participants recognized several bene?ts of planning?eld layout and?eld operations in advance(Propo-sitions1e7and9).The greening component of the new Common Agricultural Policy of the European Union(Allen et al.,2012)will probably make arable?eld margins a more important element in the funding mechanism,in which case the optimization of agricultural operations in combination with?eld margins will bene?t farmers(mentioned during an in-depth interview).The?ve interviewees agreed that the next generation of farmers will probably be more willing to accept computerized plans for?eld operations,since these plans are expected to be correct after several rounds of adjustment.

4.Conclusions

A prototype web service for the systematic planning and cultivation of agricultural?elds(GAOS)was implemented using a service oriented architecture(SOA)and standards de?ned by the Open Geospatial Consortium(OGC).It supports the creation and exchange of reference lines with two commercially available brands of auto-steering controllers commonly used in the study area.GAOS was operated by three farmers,who acted as a Geo-Information Service Point for other farmers in the Hoeksche Waard,most of whom(88%) used high precision auto-steering on their tractors.

Our?rst claim that GAOS helps farmers to plan cropped swaths and?eld margins and to automate realization of their plans was to a great extent supported by responses to the questionnaire and the in-depth interviews.In contrast,our second claim that GAOS facilitated communication with auto-steering equipment could not be sustained.Reported bene?-cial features of GAOS included saving time on?eld operations, assistance with the planning of spray paths and avoidance of overlap.However,avoidance of overlap cannot be attributed solely to the tool since it applies to auto-steering in general.

Computerized systematic planning of agricultural?elds turned out to require an interactive and iterative procedure that relies on hands-on knowledge of farming operations as well as an understanding of how the planning software functions and expertise with operating it.For effective use of GAOS,an expert agent is needed to prepare the plans for?eld operations in close consultation with the farmer and facilitate transfer of reference lines to the auto-steering controller. Enhanced user friendliness may make it possible for farmers to operate the software directly.

The rate of uptake and use of GAOS by farmers was less than we expected.This can be attributed to the following:

Operation of the software was not always straightforward and required specialized skills,which took time to acquire. The procedure followed in the tests was highly dependent on communication,which was at times inef?cient and caused delays.

GAOS did not always deliver the desired results;occasion-ally there were doubts about the accuracy of?eld measurements.

Some farmers encountered dif?culties when trying to up-load the planning results to their auto-steering controller. Farmers were sometimes not convinced of the results and tested them?rst in the?eld.

Despite this,all respondents expected the use of auto-steering equipment on arable farms to increase and most farmers identi?ed a clear demand for a system to plan refer-ence lines ahead of the actual execution of?eld operations. They stressed that such a system should be easy to operate and allow the manual adjustment of computationally opti-mized plans.In other words,both automated optimization and interactive adaptation functionality are required.While some farmers indicated a preference to operate the system themselves,most respondents(69%)preferred it to be pro-vided as a service operated by agents well acquainted with arable farming.

Acknowledgements

We gratefully acknowledge?nancial support from the Prov-ince of Zuid-Holland and the Samenwerkingsorgaan Hoek-sche Waard(through the‘Akkerbouw in groen en blauw’project)and Programma Precisie Landbouw(PPL)(through the GAOS and GAOS2projects).Many thanks go to the GISP team of Leo Klompe,Leen de Jong and Leon Noordam.Finally,we thank Aad Klompe for his undying and enthusiastic support for the project and for his help with the logistics of the questionnaire.

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