Agricultural SIS and Ethics: A Case Study

 In Case Studies

The agricultural industry employs 20% of the world’s population and accounts for nearly $3 trillion in global trade. Despite this, it is an industry that needs to grow its production levels by 70%, by 2050 to feed the world’s growing population (Schönfeld, Heil and Bittner 2016; Kamilaris, Kartakoullis, and Prenafeta-Boldú 2017). In addition, our current ecological footprint is twice the level that it should be; leaving the agricultural sector with the colossal challenge of producing more food, while reducing their ecological impact (Popa 2011; Wolfert, Sørensen, and Goense 2014). The agricultural industry is looking at different solutions to meet these challenges, one of whichis data analytics. Big Data analytics is seen as the fourth technological revolution in agriculture and it is hoped that it will provide a solution to our growing food demands (Kumari, Bargavi and Subhashini 2016; Morota et al. 2018; O’Grady and O’Hare 2017).[1]

It is predicted that Big Data analytics will take on a fundamental role in the future of agriculture (Zhang et al 2014; Carolan 2015). Agricultural Big Data, data analytics, and machine-learning algorithms are the catalysts that are expected to underpin the realisation of the world’s agricultural goals. Agricultural Big Data analytics is the analysis of large datasets from a wide range of resources, often using artificial intelligence (AI) techniques. The integration of Big Data and AI (Smart Information Systems – SIS) is expected to be vital for the successful growth of the agricultural sector. Agribusinesses are now shifting their focus towards data-driven agricultural solutions.While these developments are seen as effective ways to achieve the challenging goals ahead, they also raise a number of serious social and ethical concerns that we will analyse in this case study.

The primary research questions that will be addressed in this case study report are: Which ethical issues arise in the use of SIS in agriculture and how can they be addressed. This will be done by analysing many of the key issues within the literature on the topic and by conducting interviews with three staff members working for a large multinational agricultural organisation – BASF. The aim of this case study is to identify whether agricultural organisations are aware of the ethical issues in SIS usage; whether or not they face these issues in practice; if there are policies and procedures set in place for addressing these concerns; and do they face additional issues not addressed in the literature.

The structure of this case study will be divided into four main sections, with the first two sections focusing on an analysis of literature in the field, while sections three and four will focus on the organisation BASF. Section 1 will analyse the current implementation of agricultural data analytics and SIS technology is used in practice; while section 2 will concentrate on a range of social and ethical issues surrounding their use and implementation in the agricultural sector. Section 3 will analyse an organisation using agricultural SIS technologies: the large multinational chemical company BASF. Section 4 will critically evaluate ethical issues that arise when using SIS technologies in BASF, incorporating the three interviews done at BASF (Limburgerhof, Germany) on August 22nd 2018. However, I will first address how SIS is used in practice to give context about what some of the potential ethical issues may be during its use and implementation.

The Use of SIS in Agriculture

Before analysing the social and ethical issues that may arise from using SIS technology in the agricultural sector, it is important to understand how and why these technologies are being developed in the first place. In order to effectively understand the societal and ethical implications of using such technologies, it is vital to elaborate on the types of data being retrieved, where they are being retrieved from, and how are they being applied; otherwise, we would be making false assumptions and guesses about these technologies. This section will focus on the use of SIS in the agricultural sector in order to elucidate the different ways these technologies are used in practice and the companies developing them. I conducted an extensive literature reviews of the different ways that agricultural SIS technology is used and implemented, and on a number of agribusinesses integrating SIS within their business models in order to demonstrate how they are being incorporated in practice. It ranged from computer science, agricultural management, agricultural practice, and agricultural Big Data literature, in order to establish an exhaustive overview of the different stages of agricultural SIS technology’s use and application.

To begin with, there are many different components in agricultural SIS integration, from the retrieval, analysis and prescription of data. The types of data retrieved range from: animal movement and grazing patterns, soil moisture and nutrient level,irrigation, rainfalland climate, land imagery, crop growth patterns,and market pricing(Bennett 2015; Kamilaris, Kartakoullis, and Prenafeta-Boldú, 2017). This data is retrieved from many different sources: weather stations, surveys, static historicaldatasets, geospatial data, satellite imagery, farm sensors, farm equipment sensors, radiation sensors, climate sensors,and GPS-based field maps(Tzounis et al. 2017; and Ribarics 2016). This data is the applied in a number of different contexts: weather and climate,land,animal research,crops,soil,weeds,food availability and security,biodiversity,and farmers’ insurance and finance(Kamilaris, Kartakoullis, and Prenafeta-Boldú, 2017).

Despite the potential value of applying agricultural Big Data in all of these contexts, the amount of data that is currently being analysed is still relatively small (Hirafuji 2014). This is set to grow rapidly because of the abundance of benefits promised by the effective use of agricultural Big Data. These include: improve water and air quality, improved soil health, food quality and security, protection of biodiversity, improvements to quality of life, increase output, cost reductions, crop forecasting, andimproved decision-making and efficiency (Castle, Lubben, and Luck 2015, Mintert et al. 2016, O’Grady and O’Hare 2017, Schönfeld, Heil and Bittner 2016,and Sonka and Cheng 2015). SIScan help during the planting stage to maximise crop yields, or to react to diseases, animal ill-health, or unfavourable climatic conditions. SIS can also assist farmers in managing their farms through effective ‘prescriptive farming’ (Antle, Capalbo and Houston, 2015). Therefore, many propose that ‘good farmers do not follow their gut, they follow data’ (Carolan 2015, p. 11).

Monsanto estimate that data analytics will increase global crop production by $20 billion annually (Bunge 2014). Therefore, traditional agricultural businesses (e.g. machinery, seed or chemical companies) have branched out into data analytics, to become agricultural technology providers (ATPs). They sell prescriptive analytics to farmers: ‘The farmer generates (or hires the dealer or a third-party company to generate) data on field-specific attributes such as GPS-coded soil sampling and field maps for selected plots of land’ (Sykuta 2016, p. 60). Using different machine-learning algorithms and data analytics software, along with a wide-range of the company’s datasets and acquired datasets, the farmer’s data is transformed into prescriptive recommendations by ATPs.

There are many different agricultural companies implementing SIS technology in their programs. For example, Monsanto’s FieldScripts® program retrieves data from the farmer and combines it with Monsanto’s agronomic knowledge and prescribes recommendations to the farmer. ‘The dealer and Monsanto’s field agents help monitor performance through the season and advise on field management needs. At the end of the season, the farmer submits yield data to help improve future prescriptions for the field, which Monsanto can incorporate to update its basic algorithm as well’ (Sykuta 2016, p. 61).

DuPont Pioneer’s Field360™ and WinField R7 programs identify hybrid seed selection, provide crop management projections and crop growth estimations, and recommended planting methods (Sykuta 2016, p. 61). Pioneer’s Field360™ Select Software ‘combines current and historical field data with real-time agronomic and weather information to help growers make informed management decisions’ (Antle, Capalbo and Houston, 2015, p. 3).

John Deere attaches sensors to its farming equipment and analyses the data collected from them, then sells recommendations back to farmers (Bronson and Knezevic, 2016). John Deere’s analytics service costs farmers $15 per acre of farmland but promises a $100 per acre increase in profits. ‘This programme gives users access to algorithms that show historical trends of soil moisture and crop level weather patterns going back 30 years. The product allows farmers to plug in different seeds and receive as output, before planting season has even commenced, what their likely yields will be that fall’ (Carolan 2015).

The integration of data analytics is often intertwined with traditional agricultural business models, for example, the seed business for Monsanto and DuPont Pioneer, and tractor business for John Deere. Many of the most notable agribusinesses are driving towards an adoption of the “smart farm” framework, fully technologised with a constant retention and application of data (Coble et al. 2018). The world’s food supply is dependent on its successful implementation, but it is important that this is done in a socially just and ethical manner. There are many pressing social and ethical concerns in the literature relating to the implementation of smart information systems within the agricultural domain. It is important to analyse these in order to identify which ethical issues arise in the use of SIS in agriculture and how can they be addressed. It is also important to identify these issues for comparative purposes with the issues highlighted in the interviews with the three members of BASF, later in this paper.

Ethical Issues of Using SIS in Agriculture

Throughout my background research into the ethical and societal issues of using agricultural SIS technology, it became clear that there was very little academic work done in this area. In the key journals on agricultural and environmental ethics, there was no research done on the ethics of SIS in the agricultural industry. These journals included ‘Agriculture and Human Values’, ‘Journal of Agricultural Ethics’, ‘Journal of Agricultural and Environmental Ethics’. ‘Environmental Values’, ‘Environmental Ethics’, and ‘Ethics, Policy, and Environment’. Following from this, I conducted a broad keyword search, using multiple different variations to attract relevant articles pertinent for my background research, achieved by collating literature from bibliographical databases: Google Scholar, ScienceDirect, Web of Science and Scopus.

One of the few articles written on the topic of ethics of using SIS in the agricultural industry was Sykuta’s 2016 article‘The Ethics of Big Data in Big Agriculture’. This article was cited by 19 papers that Isubsequently analysed. In addition to this, I identified relevant papers through the bibliographic sections in these sources, as well as the “recommended articles” section on ScienceDirect. After reviewing these articles, and a number of more papers found subsequently, it became clear that there were a number of ethical issues being discussed within the debate. These included data ownership; accuracy of data and recommendations provided from algorithms; employment issues; economic issues; privacy and security; transparency and responsibility.

Accuracy of Data and Recommendations

The primary purpose of integrating SIS technologies within the agricultural sector is to provide better decisions, adaptation and prescriptions (Talavera et al. 2017). However, some claim that machine learning is not fit for purpose because the algorithms used are only suitable for small datasets (Zhang et al. 2014). These algorithms cannot effectively analyse Big Data or large datasets because of their inability ‘to strike a balance between timeliness and accuracy of processing’ (Zhang et al., 2014, p. 141). This is a technical limitation that needs to be addressed within the agricultural sector, and more broadly, the SIS industry as a whole. Limited data may also create misleading conclusions. ATPs may provide prescriptive recommendations that cause detrimental outcomes, which are based on incorrect or inaccurate data (Taylor and Broeder 2015, p. 13).

There is also a possibility that the retrieval of data is misleading or inaccurate because of environmental circumstances. For example, animals may interfere with SIS technologies by affecting the radio signals used to communicate by being too close to sensors or interfering with the equipment (O’Grady and O’Hare 2017). Sensors need to be shielded against damage, but there are also concerns regarding circumstances that give false readings, such as temperature extremes, humidity and/or animal interference (Tzounis et al. 2017). Possible interferences need to be considered to minimise false readings, skewed analytics and misleading prescriptions.

Another potential issue is that data may be difficult to interpret because of local differences or idiosyncrasies (Byarugaba Agaba et at. 2014, p. 21). Therefore, there is a clear need to analyse data contextually to make unbiased decisions (Taylor et al. 2014). If these differences are not factored into the prescriptive analysis, it may lead to lost resources, equity issues and harm to the farmer’s livelihood. ATPs also need to be confident in the accuracy and legitimacy of information provided by farmers in order to provide appropriate recommendations (Lokers et al. 2016). One of the possible reasons behind this inaccurate data is a result of farmers’ fear surrounding who owns the data and how it will be used afterwards.

Data Ownership and Intellectual Property

There are concerns about distribution of farm data to third parties (Rosenheim and Gratton 2017, p. 403). Farmers fear that their data may end up in the wrong hands and subsequently used against them (Ferris 2017). Some farmers worry that if they surrender their data, it will put them in a precarious position in the future (Coble et al. 2018, p. 84). They are concerned about the collection and dissemination of their data to regulatory bodies, agencies and governmental officials (Sykuta 2016). Their data may be used against them in a wide array of different contexts, such as regulatory enforcement, imposition of charges, fees, fines, and restrictions. There is also the concern that their data will be used by commodity traders on the stock market (Ferris 2017).

Therefore, one of the most contentious issues relating to SIS implementation is regarding data ownership (Schönfeld, Heil and Bittner 2016). Essentially, ‘who owns the data and who can monetize [the data]’ (Kamilaris, Kartakoullis, and Prenafeta-Boldú, 2017, p. 29). The issue of data ownership raises the question ‘whether farms should relinquish control of farm data to third parties’ (Coble et al. 2018, p. 84). There is the concern that farmers’ data will be used to sell unnecessary products back to them (Ferris 2017). ‘Big agricultural firms such as Monsanto might influence farmers to buy specific seeds, sprays, and equipment and are likely to profit from the costs of their service and higher seed sales’ (Ksetri 2014, p. 13). There appears to be an opacity about who owns the data retrieved from farms and who has control over their use and implementation (Kosier 2017).

Many ATPs insist that farmers own their data, but the ATPs may include a royalty-free license over these data, so they can be used by the ATP regardless of ownership (Darr 2014). If farmers own their data, and they want to change to a different ATP, they may be in breach of contract. For example, Monsanto have tight legislative controls over their intellectual property and data analytics, and if a farmer breaches their contract, this may lead to penalties and/or court-cases against them. Furthermore, if a farmer is looking for a different ATP, it may be difficult or even impossible to find another ATP because of data ownership issues: ‘ATPs may have concerns about receiving data from farmers that the farmer herself does not own, giving rise to potential violations of intellectual property or licensing restrictions’ (Sykuta 2016, p. 66).

Fundamentally, ATPs need to protect their intellectual property rights and investments in SIS. One way to ensure this is through contractual agreements with farmers. However, in the United States ‘fewer than seven percent of small-scale and medium-scale farms used contracts while over 50 percent of very large farms used contracts’ (Sykuta 2013, p. 19). The use of SIS technologies may force smaller farmers into contractual obligations with ATPs. Many of these farmers have no experience with legal documents and contractual terminology, so there is the possibility that farmers will not understand what they are consenting to when using SIS, raising ethical issues around sufficient informed consent to enter into these agreements.In addition to this lack of knowledge about legal descriptions, there is also the concern that the technologies themselves are beyond the average farmer’s capacity.

Economic Factors and Inequalities

Data retrieved from farms is often inaccessible to farmers themselves, with many fearing that they do not have the technological capacity to use SIS (Sykuta 2016, p. 60). Technical knowledge is required to interpret this data, and farmers may not be able to get this knowledge for free, and so become dependent on ATPs (Schönfeld, Heil and Bittner 2016). There is also the possibility that the role of farmers will be reduced, and a lot of associated freedoms curtailed, because of data analytics (Wolfert et al. 2017). Farmers have already started to see restrictions imposed on their land and farm machinery: Companies such as John Deere have implemented policies that disallow farmers repairing or fixing their own machinery, as it is may infringe upon copyright and intellectual property as the company’s hardware is contained on/within the vehicle. Any tampering with these devices is hence a breach of contract, and subjugated to economic penalties (Carolan 2015).

Small farms far exceed the number of large farms globally. In the United States alone, 66% of all farms do not exceed $1 million in annual sales (USDA NASS, 2014). In LMICs (low-to-middle-income countries), most agriculture occurs on small farms with very little technology. However, most agricultural data analytics is only being done on large monoculture industrial farms (Carbonell 2016). This may cause an issue of disproportionate growth of larger farms and the potential dissolution of smaller farms. The use of SIS technologies is relatively expensive, which may also prevent poorer farmers from adopting them (Kosier 2017, p. 11, Schönfeld, Heil and Bittner 2016). This leads to a ‘digital divide’ between those who can implement SIS and those who cannot (Kamilaris, Kartakoullis, and Prenafeta-Boldú, 2017, p. 29). Rural remote locations may also suffer from data transmission limitations, which could prevent farmers from using these technologies. SIS technologies hence have the potential to create or exacerbate inequalities between those who can use them and those who cannot (Poppe, Wolfert and Verdouw 2014).

The agricultural sector is the largest employer in LMICs and requires substantial growth in the coming years to meet increasing food demands. In these countries, yields are often reduced by up to 40% because of poor farming techniques, lack of information and incorrect planting, weeding and harvesting times (Ksetri 2014, p. 10). Therefore, one of the biggest areas of potential for SIS is in LMIC countries (Ksetri 2014). There is hence a push towards transforming unstructured data into implementable goals for LMIC development (Global Pulse 2012, Panicker 2013) and the UN has proposed that significant development in LMICs will be the result of effective data analytics and the implementation of their results (Micheni 2015). However, there are many obstacles impeding SIS adoption in LMICs, such as the lack of technical capabilities to analyse and formulate this data in developing countries, lack of investment, poor technological infrastructure, and political and economic instability (Micheni 2015). Furthermore, privacy and data protection laws are quite scarce or non-existent in many LMICs, so the collection and processing of data remain essentially unregulated (Taylor and Broeder 2015, p. 15).

Privacy and Security

Even though information about individuals could potentially be anonymised, it may still lead to negative repercussions for groups of people. Authorities and corporations can draw conclusions and implement courses of action at a group level. Essentially, ‘it is precisely being identified as part of a group which may make individuals most vulnerable, since a broad sweep is harder to avoid than individual targeting’ (Taylor 2017). This is particularly pertinent in LMICs, where there is less data protection regulation. For example, in sub-Saharan Africa, only 8 out of the 55 countries have data protection legislation (Taylor 2017). In the agricultural sector, this data could be used nefariously by corrupt governments, competition, or even market traders.

At present, there is very little regulation on agricultural data (Ferris 2017). It is claimed that Big Data in the agricultural sector is not as vulnerable to privacy and security concerns as other sectors (Zhang et al. 2014). This is because ATPs do not collect obviously sensitive data, such as information about children, banking data, or healthcare records, so they do not require the same level of protection (Ferris 2017). Despite this, farmers still provide a wide range of details about their farm. Personal information relating to names, locations, property types, income levels and valuations are retrieved for processing (Ferris 2017). There is also a concern that drones, and other data-retrieving technologies, will monitor third-party individuals, infringing upon their personal privacy (Schönfeld, Heil and Bittner 2016).

Big Data is retrieved from many different sources, such as radio equipment, agricultural information websites and mobile terminals (Zhang et al. 2014). As a result, there are a multitude of potentially sensitive data types that need to be stored and transferred, so security is a very important concern for farmers (Tzounis et al. 2017). Farmers need to be assured that their data are safe, used appropriately and interpreted in the correct manner (Lokers et al., 2016). However, this is difficult to universalise because the ‘nature of data security issues also differ by vendor given their services and platforms’ (Sykuta 2016, p. 60). Also, the type of data that is retrieved varies in terms of security needs, for example, securing data about a farmer’s sales and yields may be far more sensitive than data about rainfall levels on his farm.


The implementation of sensors, robots and other devices on farms may cause undue stress or harm to farm animals and external wildlife. These electronic devices and sensors may upset, injure or even kill livestock and/or local wildlife. Robots, sensors and unmanned aerial vehicles (UAVs) also have the potential to emit toxic material, fumes and waste into their surrounding environment. An additional concern is that the algorithmic prescriptions used by such devices may cause detrimental effects because they do not consider land external to the farm (Antle, Capalbo and Houston, 2015). For example, some potential effects could be surface water run-off, encroachment on habitats, or general pollution to the surrounding area. Therefore, the ecological and social effects of implementing and deploying SIS technologies to the wider environment are significant factors (Kosier 2017). As will be shown in the case study on BASF, sustainability concerns are high on their agenda, with a dedicated Sustainability Department and component within Maglis.

BASF: The Case of a Large Multinational Company Using SIS in Agriculture

The following sections will focus on a specific company (BASF) that implements and uses SIS technology within the agricultural sector. It is important to identify how SIS is integrated in practice and to evaluate if the ethical issues raised in the literature correspond to those understood and addressed in reality. In order to do this, I did background research about them; how they use and integrate SIS technology; and conducted three interviews with BASF staff members. During these interviews, I discussed their interactions with SIS and what they view as some of the most fundamental issues pertaining to this technology for their company.The interviews were conducted on August 22nd 2018 at BASF Crop Division Headquarters in Limburgerhof, Germany.

The three interviewees were with Dr. Martin Schäfer, Dr. Markus Frank, and a computer scientistworking in BASF. Their interviews were transcribed a month later and evaluated in early October. I used a qualitative analytics software tool (NVIVO) in order to categorise, define, and evaluate the content of the three interviews. Topics were split into different nodes during a two-day SHERPA consortium workshop to evaluate the 11 case studies that we were working on. We established a range of sections pertinent to SIS technology. The interviews conducted at BASF were then segmented and categorised within these nodes, which were analysed to produce this report.

Table 1.1.BASF Interviewees Working on SIS Technology

BASF Interviewees Working on SIS Technology (Maglis Project)
Dr. Martin Schäfer
Dr. Markus Frank
Computer Scientist
Reference in Case Study
Computer Scientist
Role in Organisation
Global Governmental Affairs & Management
Foundation Division
Length of Interview
40 minutes
35 minutes
55 minutes

Description of BASF and the Interviewees

In 2016, BASF generated sales of €58 billion, which $5.6 billion was from the Crop Protection Division. BASF finalised the acquirement of $5.9 billion of Bayer’s herbicide and seed business in 2018 (BASF 2017c). Part of this acquisition is a range of data compiled by Bayer. In 2017, BASF were very active in data acquisitions and partnerships: They entered a satellite data-sharing partnership with the European Space Agency, a development and operations agreement with the data insights company Proagrica, and the acquisition of the agricultural business intelligence systems specialist, ZedX Inc. (BASF 2017a, BASF 2017b, ZedX Inc., 2017a, and ZedX Inc. 2017b). BASF has traditionally been a large multinational chemical company, but over the past decade has grown its Crop Protection Division and data analytics business, such as the Maglis platform.

The interviewees from BASF Crop Protection Division wereDr. Martin Schäfer, who works inGlobal Governmental Affairs and Issue Management; Dr. Markus Frank, who works on the sustainability component of Maglis; and a computer scientist working on the backend running of Maglis. Martin’s role focuses on the advocacy of digital initiatives in the company and ensuring with different stakeholders, such as policymakers and governmental bodies. Markus is

‘the architect of algorithms which translate farming practices into sustainability language, and then back into, hopefully, improving farming practices’ (Frank).

The computer scientist works in the Foundation division of Maglis, ensuring the functionality and usability of the backend systems of the project, such as user management systems, managing users.

Description of SIS Technologies Being Used in BASF

BASF’s data analytics company Maglis™ was launched in 2016 to provide farmers with a range of crop management options within one comprehensive platform (BASF 2016, Infosys 2018).[2]The Maglis project started in Canada and has also been launched in the UK, subsequently. It is currently in the process of being launched in Germany, as well. It is intended to complement their Grow Smart programme, by personalising the exact purchase needs of the farmer.[3] BASF are collaborating with the tech company Infosys, and they have also entered a three-year partnership with ZedX Inc., to work on the Maglis project (Bedford 2017). ZedX Inc. retrieves weather conditions, wind speeds, crop protection, and pest and agronomic data It then analyses these data to produce effective agricultural solutions for farmers (BASF 2017d). By combining these data with the company’s agronomic knowledge and individual farmer data, it will provide solutions to ‘help farmers use their resources more efficiently and sustainably’ (Strip-Till Farmer 2017).

Maglis consists of three fundamental components: Agronomic Advice, Farm Navigator and Customer Navigator. Maglis Agronomic Advice provides farmers local weather predictions, plant disease in situ detection, and recommendation tools to minimise risk (BASF 2018c). Maglis Farm Navigator provides farmers with crop and yield previews, farm efficiency and sustainability metrics, and early detection systems for weed, pests and disease (BASF 2018c). Farm Navigator is used by the farmer to manage the agronomic activities of their field, by inputting when they seeded particular crops, what kind of crops, and the Navigator will provide outlines of how that crop should grow. Farm Navigator has large modelling potential and image recognition, that allows the farmer to see the growth cycle of his crop and to also detect potential threats in the area to the farmer’s crop. Maglis Customer Navigator is a support system for farming consultants that are working with farmers to find ideal agricultural planning solutions (BASF 2018c). The Customer Navigator is a Windows tablet application and is used by BASF advisers to engage with the farmers and to make recommendations about what products might help them. The software can be tailor-made for each farmer, by factoring in a multitude of data from their location, soil types, crops grown, weather predictions, and farm size.

BASF is using image-recognition software that receives images from the farmer, identifies the particular crop that it is, and can recognise spots or abnormalities on the leaves for early disease detection. This software uses machine-learning techniques to analyse weather data to ‘anticipate crop threats such as pests and disease’ (Bunge 2017).The computer scientist as BASF told me that the Maglis team are using a wide array of different Big Data and machine-learning tools, such as Hadoop Stack, SAP HANA, and cloud-based systems. The Maglis software also benefits from using data derived other sources, such as field data from John Deere machinery and AI tools, such as PEAT image recognition. The interviewee, Martin Schäfer, stated that BASF has also invested in a Swiss start-up, ecoRobotix, through its venture capital branch. ecoRobotix are developing

‘a robot that is spraying micro amounts of herbicides on wheats, which is a camera-assisted, self-driving, 130kg lightweight robot that moves automatically up and down in fields, and just treats the areas where weeds begin to grow’ (Schäfer).

BASF have been providing technological solutions to farmers for over 100 years, and they view the data analytics service as the next logical step to help farmers achieve the best possible yield on their farms in a sustainable and effective manner (BASF 2016). They view the integration of Maglis as a way of reinforcing their goal of providing effective crop protection solutions. The Senior Vice President of BASF Crop Protection North America states that ‘farmers are collecting a lot of data’, and that ‘[t]hey want help in how to put that information to use’ (Paul Rea 2016). One of the aims is to provide farmers with answers far quicker than previously. Innovation Specialist, Neil Doherty, states that he was unable to get to a customer’s farm to provide guidance, but with remotely-accessible tools such as Maglis, it can be provided before incidents occur (Vogt 2016).

One of the primary motivations for using SIS technology for BASF is the ability to make the farmer’s life easier, more productive, and to save on costs (Frank). BASF wants to incorporate its sustainability paradigm through the use of Maglis technology. It allows farmers to identify their carbon footprint, their impact on biodiversity, and the environmental impact of their activities. Essentially, Maglis hopes to improve farming, not by increasing fertilizer use, but by more intelligent farming decisions and practices (Schäfer). There needs to be an investment in knowledge transfer and the use of farm management software is one way that this can be optimized (Schäfer). Because Maglis is free to use, it could potentially offer valuable services to millions of farmers around the world, something too costly if done by sending trainers to these locations.

The Effectiveness of Using SIS for BASF

All three interviewees emphasized that Maglis is still in its very early stages of development, despite being around for a few years. Their goal is to effectively communicate a wide range of recommendations to farmers. The sustainability component of the project was envisioned through a wish-list of what they would like the system to do and requirements that should be programmed into it. There is the hope that by mid-2019 users in Europe and Canada will be able to see

‘the fully fledged indicator systems for sustainability, presented in a nice way’ (Frank).

The Maglis service is supervised by human beings, carrying out regular analysis of the prescriptions that it proposes. BASF appears to understand the fallibility of such algorithms and relying solely on artificial intelligence. There is a wide array of disparities with variables that measure weather, soil and plant disease. These variants can cause a lot of difficulties for artificial intelligence algorithms (Bunge 2017). Despite the impressive data collection and analytic abilities of artificial intelligence, there may be ‘critical data points―such as crop yield or the ultimate impact of a dry spell―[which] only emerge once per year’ (Bunge 2017).

The image recognition software to detect plant disease has to be effectively trained on a very large and growing repository of images. They have created an algorithm that can be very helpful, if the data repository is sufficient. So far, they have hundreds of thousands of plant images that go into the repository. Furthermore, the system is only as effective as the datasets that it is being trained on. If the system is not trained on a particular dataset, i.e. a banana leaf, then the system will not be able to effectively identify what that image is. The system is only as effective as the datasets that it is being trained on. However, if you ask an agronomist, they will be able to tell you straight away that it is a banana leaf. Therefore, tools such as Maglis will not replace agronomists anytime soon, as there will always be exceptions where the farmer needs human expertise as a result of the lack of intelligence of machine-learning tools. The computer scientist stated that when there are issues with their SIS, it is the result of data quality issues or lack of data, and not necessarily the algorithms. The algorithms work effectively with the training data that they are provided with, if there is poor or lacking data, then you get poor results.

Also, there are natural variations from country-to-country and region-to-region, such as differences in pest management, climatic conditions, and crop types. BASF is aware that Maglis cannot establish a one-size-fits-all approach for their project and changes their algorithms for different regions.  Another constraint lies in the actual manpower required to maintain many farm management systems, requiring a large department to work on it. Particularly, when it comes to ensuring the safety and security of the farmer’s data. One important question asked when using Maglis is

‘What information can I get out of the data available?’ (Frank).

Markus said that sustainability is dependent upon data availability and the ability to extract useful and meaningful information from this data. While there are interesting questions that would help their algorithms, farmers did not want to disclose certain pieces of information; for example:

‘How much land do you dedicate to agri-environment schemes?’ (Frank).

National values have also proved to make developing universal algorithms for all countries quite challenging because of their varying needs. For example, some countries like to talk about biodiversity, so it is incorporated into Maglis’ algorithms, whereas in other countries different sustainability parameters take precedence.

The Effects on Stakeholders

The Maglis team received some negative feedback about particular aspects of Maglis and changed those features accordingly. Despite having some really interesting and useful components within the product, farmers were unhappy with those features and they were removed. One of the main challenges was creating simple and effective user interfaces, which they got help with from external companies and consultants. Traditionally, BASF has been a b-to-b company, but Maglis differs in this approach because it can also be used by the end-user. Maglis pays close attention to users’ needs and has constantly incorporated feedback and input from the farmers into the dashboard’s functionality.

This was done through focus groups and farmer associations using Maglis. Maglis was not a ‘cocreation’, according to Markus; but farmers were heavily involved in improving the tool. Markus stated that one of the main objectives of the Maglis project was to be able to make the information accessible and understandable to the end-user – the farmer. Whether or not it would be successfully adopted and used depended on how effectively they can translate its message:

‘We are nerds, we are geeks sitting here in an ivory tower and we like everyone to know all this stuff, but the real world is the world of the farmers, and we have to translate information in a way that farmers find this understandable and, to maybe some extent, appealing’ (Frank).

Because the platform is still in its infancy, the company is still working to identify how it has improved the lives of farmers and ways to improve it going forward.

‘We start a pilot with a dedicated number of farmers, let’s say 20, 50, 100 – something like that. With these farmers, we have intense interaction. We train them, we have surveys, things like that. This is how we systematically acquire feedback early on. We repeat that for every country. You could say, “We have done the Farm Navigator. We’ve done it in the United Kingdom, so we can start it in Germany directly.” No, we don’t do that’ (Computer Scientist).

They are aware of the different needs and effects of Maglis on stakeholders, so they need to carefully design it for each region. The Maglis team have worked with many different weather companies, dashboard design consultants, and advisory boards to develop Maglis. The SIS is sent for external review to ensure that it is fit for purpose and functions according to their intent.

BASF: Ethical Issues from SIS Technology

Throughout the three interviews conducted at BASF, and through desktop research conducted from the company’s website and a number of other sources, there were a number of ethical issues highlighted as a result of using SIS technology in BASF. These issues largely reflect those found within the literature, as discussed in section 2 of this paper, highlighting a great deal of correlation between academic understanding of the issues with those working in the industry. The main issues found within the literature strongly reflect those discussed during my three interviews with BASF colleagues during their use and integration of the SIS technology, Maglis; for example, issues surrounding the accuracy of data received and inputted into their algorithms, and also the accuracy of the recommendations provided by their SIS, which I will now discuss.

4.1. Accuracy of Data and Recommendations

One of the problems that BASF encountered initially was that not all farmers had data available to be evaluated because of poor record-keeping. An issue related to the plant recognition technology used to identify plant disease was the inability of some farmers to take clear pictures of the plants. If the image data is unclear, it is extremely challenging for the image recognition algorithms to determine what they plant is. Some components of Maglis, such as this plant image identification tool, are transparent about their accuracy. There is full transparency aboutits success rate.

Data retrieved from third-party weather companies may not be accurate, and micro-climatic conditions may occur in certain fields that are not represented in the algorithms. So, for instance, if the weather data being put into the plant growth algorithm is different from what is happening on the ground, there may be discrepancies with the growth predictions. The computer scientist in BASF stated that the problem usually lies with the accuracy of the data being inputted into the system, rather than the algorithms, if there are issues or discrepancies with the system.

If the weather data retrieved from weather APIs is inaccurate, then it may lead to inaccurate growth projections in the Farm Navigator crop growth stage predictor. However, if this happens, the farmer has the ability to update and alter this if it does not coordinate with reality. If Maglis tells the farmer that his crop is at BBCH stage 32, but it is at stage 36, they can reset and recalibrate the system. The computer scientist at BASF mentioned that they are aware that the SIS will not be perfect every time and they have taken into account methods to counteract these errors.

Maglis gives prescriptive recommendations to the farmer, but if it gives the wrong recommendations to the farmer, who is held responsible or how is this ameliorated, is an important question for the project. Martin was unsure about the protocol if there was a complaint or concern about the product, as he does not work with it directly. When discussing potential issues with the use of ecoRobotix, Martin stated that the worst thing that could happen is that crops are destroyed, but that because of the procedures set in place, error detection would either be straight away or within a day or two. Therefore,

‘damage that such a robot can do in one day is nearly negligible’ (Schäfer).

While most of these SIS are in their early development stage, and their impact and effect is quite small, it is still an important concern for the company and they stated that they are certainly taking these factors on board. Another issue that became prevalent during all three interviews was the concern around striking a balance between protecting their intellectual property, while ensuring that the farmer benefits from the use of their SIS and still retains ownership of their own data.

Data Ownership and Intellectual Property

Issues surrounding data ownership and intellectual property are very important issues with the use and implementation of SIS technology and the data retrieved to make these processes function. Therefore, the types of data retrieved is an important factor to identify. After discussing this with the computer scientist at BASF, he mentioned that there are a number of different types of personal data retrieved from the farmer:

‘we have his name, we have his email address, we have his phone number, mobile phone number if he gives it, we have his postal address if he gives it. The farmer is creating his farm, on Farm Navigator, at a certain location, so we have geo coordinates. The farmer is growing his fields, so we have – even – field locations’ (Computer Scientist).

The computer scientist also mentioned that it is a top priority of the company to securely protect this data from misuse, hacking, and used for economic or marketing purposes. He stated that the company does not use the individual farmer’s data to make comparisons between farms, and if this is done in the future, it would not be done without explicit informed consent from the farmers involved. This would be done for the benefit of the farmers, the improvement of their algorithms, and development of their SIS technology.

Fundamentally, the data retrieved and used within Maglis is for the benefit of the farmer and the improved yields of their crops and farm management. All three interviewees made it explicit that the farmer owns their own data and they can move to a different farm management system supplier, with that data, if they choose to. During the interviews, it became clear that there needs to be a symbiotic mutually beneficial relationship between the farmer and the agribusiness in order to procure data to improve SIS:

‘we are convinced that people are willing to share data if they have a benefit from that’ (Schäfer).

He also stated that there is often a fear that they will provide data to agribusinesses for the development of SIS, but

‘later on have to pay money to use the result of the interpretations of their data’ (Schäfer).

So far, it appears that the use of SIS in BASF is concentrated on adding benefit to the farmer, while the company does not appear to benefit in any real economic sense from it. It is aimed to help improve the farmer’s yield, help sustain their business, and remove some of the costs from hiring agronomists. Using agronomists can be expensive, but the company is aware that their SIS would not replace the effectiveness of human input provided by agronomists.


During the interviews, it became clear that farm management systems, like Maglis, would not replace the role of agronomists because the SIS technology is not advanced enough to account for a wide range of different variables and there are limitations within the technology itself. The SIS technology is intended to complement the human expert, rather than completely replace their position. Farmers may also prefer receiving advice from human beings, rather than receive it from an impersonal SIS technology. Some farmers enjoy and benefit from the discussion and articulation of farming needs and prefer using a human advisor to using a software tool, like Maglis. Furthermore, SIS technology is fallible and very often farmers trust the advice of a human agronomist over artificially-intelligent created prescriptions. Farmers still do not fully trust the recommendations given by AI tools, such as Maglis, so still rely on the input from agronomists.

In addition to these effects on employment, Martin also made a very interesting point about the development and use of technologies, such as SIS, in the farming industry; he proposed that the development of agricultural technology, such as SIS, would make the industry more appealing to younger generations because of the allure of using high-tech equipment and practices. The agricultural sector has seen a drop in young people working in the field, so it is hoped that agricultural technology will counteract these decreased numbers, which would also explicitly improve the economic sustainability of the industry.

Economic Factors and Inequalities

One of the key constraints in agriculture is the pressure being placed on farmers to produce more for less. There are greater strains being placed on the farmer by larger supermarket and food production companies to supply cheaper, more abundant, and quicker levels of produce. Markus determines that, unfortunately, the farmer does not have a great deal of bargaining power in this relationship. The hope of BASF is that tools like Maglis will enable the farmer to farm more effectively, thus alleviating some of the strains placed upon them. One key factor in the creation of Maglis was ensuring that it was affordable and easy to use for the farmer, otherwise it would have been rejected in its implementation phase.

The Farm Navigator of Maglis is available on the web and it can be used with most modern internet browsers and is available on Android and iOS tablet applications; however, there are no MAC versions available at present. Maglis is available free-of-charge and anyone with an internet connection can download the software(BASF 2016). One of the beneficial and essential components of Maglis is that it opens up the possibility of providing free advice to many poorer nations unable to afford agronomic advice, helping sustainable agricultural development in developing countries.

One of the main reasons for developing Maglis is the company’s foresight that this is the way the industry is moving, towards more technologically-focused and advanced approaches. However, Markus emphasized that if there were no economic incentive for the farmer, the software would be rejected. Therefore, there was a strong need to make the use of Maglis viable for the farmer. All three interviewees expressed the economic concern raised by farmers and how it underpinned the success or failure of the SIS.Another economic concern that was addressed was the fear from farmers that if they are too transparent, or if their personal information is distributed, there is the possibility of being controlled by governmental regulators, either through fines or stricter regulations (Schäfer). Therefore, BASF has a strong focus on the triad of economics, privacy, and security, with all three factors taking very strong predominance during the interviews. While there is no clear economic benefit to BASF for incorporating Maglis, it is hoped that they will be able to charge for the service in the future.

Privacy and Security

The relationship between privacy and security was a key concern for the company and it was clear that the two were very closely related, with the customer’s privacy being ensured by strong security concerns. The computer scientist at BASF told me that security was a very high priority for the company and that they develop the latest methods to ensure their system is secure:

‘Inside the system, passwords – things like that – are encrypted. For storing the data, the general mechanisms of SQL databases are used – also, for encrypting. The data is as secure as our system is, to prevent it being compromised by having an intruder there that can get his hands on the farmer’s data’ (Computer Scientist).

In order to ensure that this security is maximized, the company hires third-party organisations to test their system and coordinate attacks to find issues or problems in the system. They are aware of issues surrounding reverse engineering to access the algorithms in Maglis, so they put great detail into securing all of their platforms. If BASF’s servers are secure and Maglis is fully encrypted and secured, then there is little chance that farmers’ data will be breached. They place a strong emphasis on security in order to ensure customers’ privacy. The Maglis project works within BASF’s code of ethics, so abides by BASF’s use of customer data and other regulations. Martin emphasized that they aim to promote transparency and legitimacy within Maglis,

‘to make sure that what we defend in public is what we want to see also within the company’ (Schäfer).

One of these goals is the strong emphasis within the company on sustainability, putting a dedicated focus into ensuring that sustainability goals and parameters are implemented into the SIS.


There were no customer requests to have a sustainability component put into Maglis, but the company viewed it as an important factor to integrate within the SIS. Markus pointed out that farmers did not see its relevance, but once it was explained that it can be used to comply with different regulatory procedures, it was more widely accepted. Markus said that the Maglis project is aligning sustainability certification schemes within it in order to help farmers meet these certifications. The sustainability criteria was developed from the European PEF and the company’s LCA frameworks. However, he is aware that countries will have different quality sustainability standards, so Maglis needs to account for these differences.

The Maglis project was launched in Canada and focused on providing advice within a Canadian context. BASF were aware that the algorithms used for Canadian farmers could not be universally used for all farmers everywhere. Different algorithms are required because of the varying climatic conditions, crop types, and needs of farmers worldwide. Markus stated that sustainability needs are local and require different sustainability parameters and there was a trial-and-error process in the beginning to fix bugs, as Maglis was a prototype. All three interviewees emphasized that this SIS was still in its early stages of development, but the company is working hard on resolving any issues that may arise. They are taking a fresh perspective in the industry by incorporating a sustainability component within their farm management system to anticipate future constraints placed on farmers, the environment, and society as a whole, in the future.


Despite the advancements and progress that has been made in the agricultural sector, there are still many social and ethical issues during the integration and use of SIS technologies on farms. This case studydemonstrated howSIS is being used in the agricultural sector, why they are required, and the relationship between farmer and ATPs. The three interviews with BASFemployees offered perspectives into the real-world application of farm management systems and addressed a number of practical, economic, and ethical issues through the use of SIS. The research question was to ask which ethical issues arise in the use of SIS in agriculture and the following were identified: accuracy of data and recommendations, employment, economic issues, ownership of data, privacy, security, and sustainability. These issues were addressed in a number of ways by BASF. For example, they expressed awareness that there are going to be economic constraints using SIS technologies, so they offer them free-of-charge, which reduces the costs farmers have to pay for agronomic consultancy; while not using the technology as a replacement for the agronomist.

The company also protects personal data stored about the farmer and constantly acknowledges privacy concerns during the use and implementation of their SIS technology. They view the security of their system as paramount for protecting this. The farmer also retains all ownership of their own data, which has been a huge concern in the literature. The Maglis team are aware of the limitations with SISand expressed the need to constantly develop their SIS modelling and improving their data quality. For instance, the plant image verification software will be trained more efficiently with larger image repositories, while the accuracy will be improved through training farmers to take better pictures. The company is also taking a very proactive approach towards sustainability agendas and issues, implementing sustainability parameters and advice to the farmer through Maglis, while also being aware of their technology’s limitations.


Apart from the effectiveness of particular aspects of the SIS technology, there were a few limitations that the company may want to focus on in the future. While great efforts are being placed into the development of SIS, there are still restrictions and limitations to its effectiveness for implementation, as a result of the changing requirements for different countries. The Maglis team are aware of this and do not intend to roll out a universal SIS, stating that it needs to be tailored to specific needs of each region it is developed. The integration of stakeholders throughout the process was commendable, but going forward, perhaps the incorporation of stakeholder input prior to the use of this SIS may be valuable for the company.

Another limitation about the SIS is the identification of when it does not work as intended, or causes adverse effects as a result of its use. It was unclear if there was an onus of responsibility on the company if negative effects occurred from using the technology. Perhaps, BASF could clarify clarification in their terms of service and providing adequate informed consent to the farmer about this, may be valuable going forward. How this SIS will develop and what type of data it may require in the future, and whether or not there will be a charge for this service in the future, are all important issues that need to be addressed and also discussed with the end-users of the service for its successful ethical implementation.

Contribution to Knowledge

Overall, the Maglis project is in its early developmental stages and the people I spoke with in BASF had a strong grasp of potential issues within the SIS and had a range of policies and procedures in places to tackle these concerns. There were correlations between academic and practical issues regarding agricultural SIS, with many of the most pressing issues already being addressed by the Maglis team at BASF. While there are areas that need to be considered going forward, it appears that BASF have identified, and are tackling, some of the most pertinent issues found within the literature. This case study has offered a valuable contribution of knowledge to the discipline of Big Data/AI ethics, while also contributing to empirical research on the implementation of SIS technologies within the agricultural sector.

Overall, there has been very little research done on the ethical, legal and social implications of using SIS technologies in agriculture, with only a handful of articles tackling these issues. There have been few articles that havecohesively addressed ethical issues of SIS technology in agriculture up to now, and this report will offer a valuable contribution of knowledge to this area. ATPs are a new business model in the agricultural industry, so there has been very little policy recommendations or controls set in place to ensure ethical practice when using SIS. Therefore, this case study will offer fresh perspectives and approaches to SIS. It will also offer valuable input into policy and agenda setting for ATPs, while also having implications for theory, practice, and policy.

Implications of this Report

This report has theoretical implications, as it offers an original case study on the use of SIS in the agricultural sector. From the extensive research that I have done on this topic, there has been very little research conducted in the application of SIS in agriculture.Also, I could find very little information on BASF’s development of SIS in the agricultural sector, besides self-published pieces on their website and affiliate organisations. The theoretical work done on ATPs is also scarce, with only a handful of published articles on this topic. While many of the ethical issues discussed in this report have been analysed more generally within academia and assessed in other areas, they have rarely been applied to the agricultural field. Therefore, this report will have strong implications for the development and furthering of agricultural theory and knowledge.

This report aims to also have implications for the practice and implementation of SIS technology within the agricultural industry. While ATPs are a relatively new phenomenon, the agribusinesses that have created them have been around for many decades. So far, the newness of ATPs leaves a lot of room for how they are implemented, controlled and evaluated in practice. It is hoped that this report will offer insights to the main players in the industry on the ethical issues found in the use and implementation of SIS. While only one ATP business was interviewed, this is still 25% of themain agribusinesses in the field developing SIS (BASF, Bayer/Monsanto, DuPont Pioneer, and John Deere). It is hoped that this report will provide feedback for the other three agribusinesses in how they develop and implement their SIS technologies.

The report will not only provide guidance to the ATPs integrating SIS technologies, but it will also provide policymakers with fresh insights into these emerging technologies. So far, there has been a handful of policy documents created to tackle issues relating to agricultural data and the integration of SIS technology in this domain. This is set to change dramatically change with the increased economic investments into ATPs. Therefore, there needs to be adequate policies and regulation set in place to ensure that ethical and legal requirements are met. This report aims to provide an example of how a large multinational agribusiness is implementing SIS in practice and how it correlates to many of the concerns found in the literature. However, we are also aware that this report does not answer every question on the topic, thus requiring further research in the field.

Further Research

While this report offers an extensive literature review of the most pertinent ethical, social and legal issues of agricultural SIS, there may be additional matters that need to be evaluated in the future. The literature review provides a comprehensive documentation of the most pressing ethical concerns in the field in relation to SIS technology in agriculture, but further research may berequired to uncover additional issues in this emergent field. This report evaluated a number of empirical studies conducted with farmers, the use of SIS technology, and their relationships with ATPs, but further empirical research in these areas would provide valuable insights and contributions to the domain. Furthermore, the field would also greatly benefit from additional case studies with the other three ATPs in this area (Bayer/Monsanto, DuPont Pioneer, and John Deere).

While we covered BASF’s integration of SIS technology, the ethical issues pertinent within the other three ATPs may be quite different than those found in this report. It would be interesting to have these additional case studies available at some stage in the future in order to cross-examine the varying styles, implementations, and usages of SIS technology by different ATPs. Additional case studies are requiredto evaluate the differences between the use and implementation of agricultural SIS technology in North American (DuPont Pioneer, John Deere, and Monsanto), contrasted with European (BASF and Bayer) implementation of these technologies would also offer some great insights into the field. We hope that this report will encourage such similar case studies and burgeoning papers to emerge as a result.



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[1] The industrial revolution, the green revolution, and the biotechnology revolution.
[2]BASF has embarked on two major data analytics projects: Compass™ and Maglis™ (Stroud 2018). Compass™ mobile farm data management system integrates crop management, GIS/GPS mapping, grain inventory management, and live agent support; to provide farmers with better farm management decisions (BASF 2018a). However, this data management system was ‘transferred and assigned to Affinity Management Ltd.’ on May 8th, 2018 (BASF 2018b).
[3] The Grow SmartTM with BASF programme states that farmers have limited chances to establish the right mix during each season in order to ensure the best possible yields are possible. There needs to be careful agronomic knowledge, careful planning and portfolio innovation to procure the best possible approach each year (BASF 2018e). The BASF Grow SmartTM University is their platform that provides the resources of agronomic knowledge and insights for the most productive and effective options the farmer should take (BASF 2018d). This website provides a range of free informative courses, webinars and videos on many different seed specifications, herbicides, insecticides, plant health, seed enhancement and agribusiness/market-place economic forecasting. Ultimately, BASF attempts to ‘support farmers through several knowledge transfer initiatives’ (Heldt 2018).

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