Introduction

Data is the foundation of digital agriculture and agricultural data are generated from various sources, such as human observations, sensors in the environment, unmanned vehicles and machinery, and satellites (Martos et al., 2021; Wang et al., 2020; Wolfert et al., 2017). Vast amounts of data are generated from the initial planning of farming when supplies are purchased; it continues during soil preparation, planting, within-season treatments, and continues after harvest. These data have the potential to weave a comprehensive narrative about production that can also be valuable downstream in processing. Descriptive agricultural metadata (often behind-the-scenes operational information) provide a deeper understanding of the entire operation. While cost, profit, and yield data are useful for financial optimization, many more agricultural data captured in farm records can provide valuable insights in logistics, traceability, and marketing. Such data, including detailed records of who is doing what activity, when, and on which field, can only be gleaned from on-farm records (Hess, 2022). However, the nature of this data is generally descriptive, relying on manual recording methods that introduce the potential for error and data gaps, and inconsistencies (Witkowski et al., 2024; Wixted, 1998). Consequently, the analytical process is impeded by the need for meticulous, and at times arduous, data cleaning procedures. Considering that such data is utilized in the development of agronomic models, decision support tools, and other domain-specific digital technologies for site-specific management, data accuracy and consistency emerge as vital concerns. Accuracy in on-farm data is necessary for precision in operational planning and farm management (Sreekanth et al., 2013). This precision, facilitated by the practice of regular on-farm recordkeeping, underscores the pivotal role of meticulous data maintenance in agricultural decision-making and long-term strategizing (Robinson, 2005).

Humans have been keeping written records since the early days of mass-cultivation of crops: some of the earliest evidence of writing, from ancient Mesopotamia in the Late Neolithic period, includes the tallying of grain and other forms of agricultural recordkeeping (Goody, 1986). As human civilizations grew and evolved, so did the complexity of our writing and methods for recording data. The development and maintenance of simple and easily usable on-farm recordkeeping schemes were recognized in the UK over one hundred years ago by Dymond (1903) as the main means of improving agricultural productivity.

Hand-written notes and paper-based records are still common in modern agriculture (Raturi, 2017), but the need for digital recordkeeping is being increasingly realized. As agricultural data begin to get integrated across a myriad of data sources and collected by cross-functional teams (e.g., farm managers, farm workers, service providers, crop advisors, and researchers), there is an increasing need for integrated and contextualize agricultural data with user-friendly and interoperable digital technologies (Raturi & Buckmaster, 2019). Farm management information systems (FMIS) and applications handle a lot of labor-intensive recordkeeping, resulting in a more seamless experience (Kaloxylos et al., 2012; Salami & Ahmadi, 2010). Digital platforms allow for easy storage, archival access, and distribution of data to team members (e.g., among a farm crew, within supply chains) and diverse audiences (e.g., regulators, consumers) (Tang et al., 2002).

Utilizing digital platforms for farm recordkeeping offers numerous advantages. The foremost benefit lies in enhancing collaboration among farm teams (Chauhan & Kumar, 2013). These platforms provide an accurate representation of the farm by visually mapping operational records across time and space. By following historical activity records, seamless activity scheduling and worker task assignments become more feasible (Lee et al., 2014). Reports on activities and efficient tracking of inventory or farm assets are vital for informed decision-making in farm management (Baumüller & Kah, 2019; Daum et al., 2021; Fabregas et al., 2019). Concurrently, the digital framework promotes interoperability (Runck et al., 2022) fostering comprehensive awareness among all stakeholders regarding field activities. In sum, the adoption of digital recordkeeping platforms presents a transformative landscape for agricultural management, augmenting collaboration, precision, and decision-making.

The evolution of farm recordkeeping systems portrays a scenario wherein both paper-based and digital methods coexist. The ongoing development of digital recordkeeping systems aims to resolve data interoperability challenges, enhancing the utility and shareability of on-farm activity records. This also minimizes errors and ensures the abundance of valuable information. To facilitate the transition and improvement of these systems, a thorough investigation into the diversity of existing recordkeeping practices is imperative. Hence, to address the diverse and evolving nature of on-farm recordkeeping systems, the objectives of this state-of-art study are to (1) characterize and categorize existing on-farm recordkeeping systems to overview their strengths and limitations comprehensively, (2) explore on-farm recordkeeping computer programs and smartphone apps and critically analyze their features and traits, focusing on interoperability and efficiency and (3) discuss an ideal data-taking system that addresses drawbacks identified in existing methods, emphasizing interoperability, accuracy, and completeness.

Current agricultural data systems lack the necessary plenitude to drive artificial intelligence. The data sets and streams from sensors, imagery, and machinery data collection platforms lack full context for robust analysis. This manuscript critically assesses the strengths and weaknesses of current farm recordkeeping systems and includes an effective solution to address identified challenges. The suggested system promotes interoperability through modern technologies, offering a significant contribution to the resolution of longstanding issues in agricultural recordkeeping.

Categorization of farm recordkeeping methods

Farm data acquisition falls into two categories: human and machine (Raturi & Basir, 2022). Although farm activity records could be interpreted from machine data (Sharma et al., 2013), currently farm activity records are incorporated in the human input section which is subdivided into paper-based and digital methods. According to Jones and Collins (1965), recordkeeping books differ in terms of their function, types, and the nature of the records. They can be cash books if financial components are included, labor books if labor engagement is a focus and labor wage calculation is an objective, or cropping records if all cropping operations are recorded (Jones & Collins, 1965). The record books might also change depending on their intended usage. For example, a farm manager’s recordkeeping notebook likely differs from a field worker’s notepad. Fountas et al. (2015) provided a comprehensive classification of FMIS, where they discussed various types of data records with distinct purposes. Daily farm records for monitoring essential agricultural operations and activities play a crucial role in evaluating past farming efforts and facilitating future planning. Additionally, the authors discussed farm implement and equipment records, which document the farm’s equipment inventory, purchase dates, and relevant descriptions. The study also emphasized the integration of production records, farm usage records, farm expenditure records, workforce and human resource records, transportation records, and sales records for effective whole-farm management and economic interpretations.

Digital modes of recordkeeping include online (remote or cloud) storage and local (computer hard disk, USB drive, local files) storage. Digital modes of recordkeeping may serve the same purpose of recordkeeping (tally events, inputs, etc.) but in the added traits of electronic, streamlined, and tidy, digital modes enable additional applications or repurposing.

Records of farm activity can be of different forms such as numeric, descriptive, textual, image, audiovisual, or audio (Doye, 2004; Junaid et al., 2021; Mahfuz et al., 2022). Images are used in support of the authentication of real conditions. Audio data collection makes record collection easier but requires more processing (whether human or AI) for interpretation and use. Descriptive and textual, ideally quantitative whenever appropriate forms can be readily aggregated and summarized to provide insights.

A framework representing a categorical clustering to understand the different types of agricultural recordkeeping methods is illustrated in Fig. 1. The color-coded areas represent different recordkeeping methods with some examples in white ovals. The axis arrows represent transitions.

Fig. 1
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On-farm recordkeeping methods (Color figure online)

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Figure 1 depicts a transition from unstructured to structured method of recordkeeping and concurrently a similar trend of transition from paper-based to digital. The takeaways from Fig. 1 include:

An apparent shift from paper-based to digital recordkeeping methods Digital recordkeeping systems have advantages over paper-based systems in terms of ease in query, retrieval, transfer, and interoperability. These reasons encourage a transition from paper-based to digital recordkeeping systems that will likely persist into the future. To effectively navigate this transition, farmers and agricultural workers must have a readiness to embrace digitalization. It is imperative to provide them with appropriate education and resources to facilitate their adaptation to digital recordkeeping practices.

The transition from unstructured to structured recordkeeping poses challenges While unstructured methods offer convenience in field data collection, they make data retrieval more difficult than with structured methods. There is a growing trend towards structured means for long-term data storage alongside the continued use of unstructured recordkeeping systems, which anticipates a coexistence of both systems in the future. To address both systems’ efficacy, developers should emphasize tools with improved user-friendliness and accessibility. Collaborating between farm stakeholders, educators, and developers is imperative to understand their specific needs and develop tailored solutions.

Farm recordkeeping is moving toward an optimized system Digital recordkeeping outperforms paper-based systems, but unstructured and structured methods persist for their unique advantages. Ideally, the trend of on-farm recordkeeping will move toward a digital system having the ease of unstructured systems yet the advantages of structured methods. Engagement from farm stakeholders (who are related to farm work, i.e., workers, managers etc.) is essential to facilitate the transition to advanced digital technologies. Providing training and educational resources to facilitate technology adoption is imperative, with universities and extension agencies positioned to play a pivotal role. Additionally, being pivotal actors in this domain, software developers should endeavor to devise more robust and user-friendly digital tools that integrate the simplicity of unstructured methods with the efficacy of structured recordkeeping.

Approach to systematic technology review

This review encompasses paper-based and digital recordkeeping systems, including spreadsheets, software, web applications, and smartphone apps. The focus herein is to provide examples of these methods and tools, elucidate their purposes and identify use cases in agricultural recordkeeping.

The review process followed a systematic approach in three phases. In the initial phase, three prominent bibliographical archives, namely Scopus, Academic Search, and Web of Science, were explored due to their comprehensive coverage of relevant literature and advanced bibliometric features. Additionally, advanced third-party search tools such as Google Scholar, ResearchGate, and Academia were utilized to acquire pertinent documents. The search was conducted using a specific set of keywords relevant to the field of interest to comprehensively capture and encompass the domain under investigation. The keyword combination utilized included terms such as agriculture, farm, recordkeeping, bookkeeping, logbook, note taking, spreadsheet, activity, in-field operation, and notebooks.

In the second phase, information regarding digital platforms was sourced from their official websites and user manuals. Digital tools, often developed by universities and government agencies, are typically accessible through Cooperative Extension services and websites. This phase also involved reviewing grey literature (non-refereed but publicly available) and news releases. The keywords and their combination used in this phase were digital, agriculture, farm, spreadsheet, database integrated, form, 4-H, recordkeeping, and online form.

In the third phase, for smartphone apps, both the Google Play Store (Android) and App Store (iOS) were searched. Keywords used in this search were farming, app, recordkeeping, note taking, in-field, and agriculture. Additionally, web applications and software previously identified by the previous work of Capterra (2023) and Saiz-Rubio and Rovira-Más (2020) were also characterized. The search method is illustrated in Fig. 2.

Fig. 2
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Method to explore on-farm recordkeeping systems and tools

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Survey of existing recordkeeping systems

The recordkeeping systems derived from the survey outlined in section “Approach to systematic technology review”, are classified according to the theoretical framework depicted in Fig. 1. This section delves into the current recordkeeping systems, providing examples along with their respective advantages and disadvantages.

Paper-based recordkeeping system

Data acquisition by humans can be done on paper using free-form notes, structured tables (forms), questionnaires, sticky notes, or field logbooks (Fig. 1). While convenient to the record maker, the lack of automatic archive (copies) and high likelihood of not having access (at least temporarily) make paper-based systems problematic.

Free-form

Free-form recordkeeping refers to unstructured and non-predefined formats. In these recordkeeping methods, a wide variety of data such as numbers, text, or sketches describing conditions, settings, observations, inventories, etc., can be noted. The data structure might be descriptive, with headings or data labels, but that would not consistently be the case.

A documentary study by shows the recordkeeping methods of farm activities in France during the nineteenth century. The author mentioned diary-keeping as a pervasive practice on French farms. Farmers of small farms or household farms used to write down the agenda in a calendar notepad. Farm workers kept records of activity. Every farmer was given a pocket-sized memorandum-book in which to record every operation or event as it occurred, according to the date, irrespective of whether it related to cash, and the specific details should be documented in the proper book (Stephens, 1852). While there might have been a date structure to such records, that would be the only aspect of structure. Bookkeeping was also mentioned in that documentary study. If the log was organized by date and there was a blank page, it was considered to be “no work”. In the entry for Agriculteur, the 1921 Larousse Agricole: encyclopédie illustrée (Chancrin & Dumont, 1921) illustrates a farmer making his rounds in his field while carrying a notebook in his pocket to take several specific notes: labor hours, animal feed consumption, fertilizer doses, seeds, harvests, and many more. So, the farm activities were recorded using small pocket notebooks. Free-form recordkeeping provides the freedom to document information in a more natural and personalized way, enabling individuals to capture specific details that may not fit into standardized formats. It is often used when there is a need for flexibility and creativity in recording data as it allows for customization and adaptability to individual preferences and specific contexts. The downside of unstructured records is that the records are not adequately organized; to extract information, the manager must frequently comb through mounds of documents and perform all calculations manually (Roth, 2021) and this is not efficient at scale in space nor time. Free-form recordkeeping also leaves the data collection solely to the current thinking of the worker. It may very well be that something actually important does not come to mind as such and is never recorded (Wixted, 1998).

Templates and forms

Querying unstructured data for specific information is challenging, a problem mitigated by adopting structured recording formats. These systems may include tabular logbooks or forms, whether handwritten or printed, providing templates for data entry. Joly (2011) mentioned that structured notepads used in the late nineteenth century featured temporary forms with activities and objectives. In Germany, farmers used compact notebooks with multiple entries, column headers, and striped lines.

The 4-H youth development program offers agricultural activities where school-going students use structured 4-H farming forms to document tasks and expenses (4-H, 2023). 4-H record templates for crop management are also useful and handy means to document farm operations, expenses, and income; such formats also serve an educational function to highlight importance of certain elements of information. These templates for crop management are educational tools highlighting essential information elements. Oregon State University publishes horticulture record forms for flowers, ornamentals, vegetables, and herbs, combining operational data, crop status, and financial aspects (Bothum, 2018). Purdue University’s Extension-4-H Youth Development developed a crop planner and recordkeeping book for multiple crops, detailing field surveys, production practices, and financial records (4-H Youth Development, 2023). The University of Illinois Urbana-Champaign provides a comprehensive form for corn, soybean, and small grains, including soil data, weather data, labor, operations, storage, marketing, asset inventory, and crop yield records (University of Illinois Extension, 2016). These tools are designed for educational purpose but they can be good examples of integrated recordkeeping forms.

The added structure of data in templates or forms (compared to free-form text and numbers) is amenable to queries and creating insights (Robert & Atre, 2003). Structured farm books have the advantage of being simple to comprehend and well-organized for subsequent reference. These templates distinguish themselves from farm logbooks by their characteristic of short-term usage, serving as convenient and portable tools for on-the-go recordkeeping in the field. The drawback is that the information may not be presented in a preferred manner by the management, may be hard to track down, and since it is a manual-entry, handwritten accounting system, entering information may be time consuming (Roth, 2021).

Farm logbook

Recording farm activities on a farm logbook is an ancient recordkeeping process that is still utilized. The logbooks are the archive of field work by date, time, and person who conducted the operation to what extent. These books are either unstructured (free-form) or structured writing templates (Fig. 3).

Fig. 3
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Farm logbook sample for field 70 from Purdue ACRE (Raturi, 2021)

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Farm logbooks offer several benefits in agricultural recordkeeping. They provide a systematic way to document farm activities, track trends over time, and comply with regulations. Logbooks aid in planning, decision-making, and troubleshooting by offering historical data for evaluation and analysis. They enhance communication and collaboration among farm team members and serve as a valuable source of knowledge and experience. By helping farmers assess performance, meet industry standards, and make informed decisions, logbooks contribute to improved productivity and profitability. However, maintaining logbooks can be time-consuming and requires consistent effort. There is a risk of incomplete or inaccurate entries if they are not diligently updated and inspected. Logbooks may lack standardization, making it challenging to compare data across workers, years, fields, farms, or systems. Additionally, logbooks may not capture all relevant information or provide comprehensive insights into complex farming operations. The manual nature of logbooks also increases the likelihood of errors and can be prone to damage or loss. As Buckmaster et al. (2018) noted, data quality decreases if not recorded in real-time. The infrequent access to the logbook, typically kept in the office, means that data quality heavily relies on the workers’ memory and motivation, leading to potential inaccuracies and inconsistencies.

Digital recordkeeping systems

In the era of digital agriculture, knowledge transfer is important, and the main driver is easily transferable and interoperable data. Digital agriculture systems must integrate data collection, transmission, warehousing, and digitally controlled agricultural equipment to inform decisions and automated operations (Tang et al., 2002). To meet the requisites of data-driven digital agriculture, agricultural data should transcend being paper-based, ensuring not only stationary usefulness but also facilitating fluidity, interoperability, and accessibility beyond limited ownership or authority (Brandusescu & Lämmerhirt, 2018).

Digital recordkeeping can be done by local storage records such as offline spreadsheets, software, and smartphone apps whereas cloud storage records using online spreadsheets, forms, web apps, online software, and smartphone apps yield added benefits of sharing among people and firms and availability at any place and any time provided there is connectivity.

Spreadsheets

Spreadsheets are a widely favored method of recordkeeping, serving as highly versatile software tools for data entry, storage, interpretation, and visualization (Broman & Woo, 2018). They are also widely employed as decision support tools, facilitating general problem-solving and the modeling of alternative scenarios for decision-making. Prominent spreadsheet software includes Microsoft Excel, Google Sheets, OpenOffice Calc, WPS Office Spreadsheets, Gnumeric, Spread32, and SSuite Accel, with Microsoft Excel and Google Sheets being the most prevalent choices. While spreadsheet-based data structures are flexible, certain universities and agricultural organizations provide templates tailored for agricultural applications. Purdue University released a webinar series named “Dig into Data with Spreadsheets” (Purdue Agriculture, 2020). In that webinar, the speaker shows a demo on how to maintain an interactive spreadsheet record book for livestock farming. Cornell Cooperative Extension of Cornell University provides some templates of a crop expense calculator and field crop record management tool (Hadcock et al., 2017). The spreadsheet tools help farmers to record field-by-field tasks and expenses, so in the end, it can calculate the profit, expense, and income. Those tools also help with crop rotation planning, manure management, and soil sampler. Those tools are more intensified for decision support but could be adapted for metadata recording.

Apart from the universities, Carolina Farm Stewardship Association (Riddle & Ford, 2012) developed farm recordkeeping templates that keep the record of farm activities and provide economic decision support. Curtis Stone, a Canadian urban farmer made a set of learning materials that is shared through the authors urban farming blog, YouTube videos, and a book. In those videos, the creator shows how spreadsheets are used for on-farm records (Off-Grid with Curtis Stone, 2017).

Spreadsheets offer several advantages for farm recordkeeping. They provide an organized structure for managing and retrieving data; with built-in calculation and analysis functions, they can be both a database and a computing tool to provide insights (Malcolm, 1990). Spreadsheets also allow for data visualization through charts and graphs, enabling easy interpretation of trends. They are flexible and customizable to fit specific recordkeeping needs, and offer data manipulation capabilities for sorting, filtering, and extracting information. Spreadsheets can be shared to facilitate teamwork and real-time access to data (Kalbers, 1984).

While offering advantages in farm recordkeeping, spreadsheets come with certain drawbacks. Their effective use necessitates users, particularly farmers, possessing adequate skills in spreadsheet software, primarily in algebraic operations. Moreover, the flexibility of manipulating data within spreadsheets can potentially lead to inaccuracies or data integrity issues if not handled with care. Without diligent adherence to proper data protocols, such as making copies of original data, data corruption can occur. Users must exercise caution, implement rigorous data validation, and maintain comprehensive documentation when employing spreadsheets for farm recordkeeping (Powell et al., 2009; Streit et al., 2008).

Cloud-enabled spreadsheet integrated forms

Cloud-enabled spreadsheet integrated forms offer functionality of online spreadsheet applications (e.g., Google Sheets) with the addition of forms within the spreadsheet environment. Google Forms offers spreadsheet-integrated forms. Forms are interactive and editable and can be managed without typing a single line of code. Google Forms offers a user-friendly data entry environment and a spreadsheet for data storage and visualization. Community Supported Agriculture shares a tutorial for making Google Forms-based farming data collection forms (Hickson, 2020). The cloud-based spreadsheet integrated forms and generated data in a spreadsheet illustrated by Hickson (2020) is shown in Fig. 4.

Fig. 4
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(Source: Hickson, 2020)

Example of cloud-enabled spreadsheet integrated forms: a recordkeeping using forms and b generated cloud spreadsheet by

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The Farmer Spreadsheet Academy offers a set of demos on how to make and use Google Forms and visualize the on-farm data using Google Sheets (Brisebois, 2020).

Cloud-enabled spreadsheet integrated forms offer advantages in farm data collection and management by providing a user-friendly interface for respondents to directly input data, reducing errors and duplication. Additionally, the data are stored centrally in the cloud-based spreadsheet, enabling easy access and efficient organization. Real-time collaboration is another benefit, allowing multiple users to contribute simultaneously and ensuring up-to-date information (Agarwal, 2011). However, data security risks exist with cloud storage, requiring appropriate security measures. Dependence on internet connectivity may limit data collection and access. Finally, there may be a learning curve for users unfamiliar with spreadsheets or online forms, requiring time to adapt to the platform.

Hybridized database integrated forms

Hybridized database integrated forms offer a unique combination of database functionality and form-based data collection. These systems provide the benefits of both structured databases and user-friendly form interfaces. Airtable, for example, makes a spreadsheet with a flavor of database where forms can be generated with a linked and fetchable database (Airtable, 2022b; Meijer, 2022). The use of Airtable in field operational data opens a new horizon of digitizing activity or event records in a simple and effortless way. Purdue Digital Agriculture provides a set of demos on the use of Airtable for field records (Digital Agriculture, 2022). Purdue University provided an Airtable-based field record system that includes an interactive form that was automated to make it easy to use for farmers.

Similar to Airtable, the Kobo toolbox offers data record forms integrated into databases. Precision Sustainable Agriculture (2020) in their demo video, shows how to use their format of recordkeeping using the Kobo toolbox. Kobo can also be used for recording data from sensors and can be integrated into form-based data collections. Both Airtable and Kobo provide data in interoperable format. Both offer an API to communicate with other applications and data can be pulled for further use. The difference is, Kobo can work offline and it is open-source (Kobo Inc., 2022) while Airtable only offers online availability and it is not open-source (Airtable, 2022a).

A paid survey form builder platform that offers similar scope to Airtable and Kobo is NestForms (Nest Design Limited, 2022). The use of NestForms in digital farming recordkeeping was documented by Campbell on NestForms’ official website. It offers recording voice annotations and images as an extension of regular recordkeeping forms that can be built using NestForms’ drag-and-drop tool. Forms built by this tool can be operated offline and synchronized with a remote database (Campbell, 2022).

An open-source tool named SurveyStack (2023) is being used for data acquisition in different agricultural aspects; from recordkeeping of farm activities to agricultural research. This tool is being used for gathering and accumulating data from different sources too (Proofing Future, 2021). The tool was developed for supporting the community collecting agricultural data with a huge collaboration of farmers, extension workers, researchers, agronomists, soil health coaches and anyone who wants to contribute as an agriculture community member.

NocoDB (2023), Mathesar (2023), Rowy (2023) and Grist (2023) exemplify open-source tools that offer functionalities akin to Airtable and Kobo. Although these open-source platforms currently lack widespread recognition among end-users of farm recordkeeping systems, especially within the farming community, they harbor substantial potential to garner attention in the future.

Hybridized database-integrated forms offer a structured and organized approach to data collection and management, resulting in enhanced data accuracy and integrity. They minimize the need for manual data entry, reducing the potential for errors and inconsistencies. These systems also provide flexibility, allowing users to design forms with various field types to capture specific data accurately. Real-time collaboration features simplify teamwork by enabling multiple users to work simultaneously on data entry and management. However, challenges arise when creating complex forms, and ensuring seamless connectivity to cloud databases is often an issue, though some tools do offer synchronized online and offline modes (e.g., Airtable).

Farm management software and web apps

Farm management software and web apps are preferred over paper-based recordkeeping due to their seamless database integration and user-friendly interfaces, enhancing record accessibility efficiency. As agriculture undergoes digital transformation, farmers are faced with the task of collecting and analyzing data from diverse sources and devices to boost production and operational efficiency (Csótó, 2010). Managing the vast and interconnected dataset generated in modern farming necessitates robust database integration. Effective interaction with databases relies on user-friendly and efficient front-end interfaces provided by software and web apps. The digitalization of agriculture is exemplified by the introduction of FMIS software and web apps, which offer comprehensive farm recordkeeping capabilities. Some popular software and web apps are described below.

Ag Gateway’s Agricultural Data Application Programming Toolkit, ADAPT, an open-source program, is designed to facilitate the integration of machine control systems and FMIS (“A Boost to Interoperability in Precision Agriculture:AgGateway’s Agricultural Data Application Programming Toolkit (ADAPT),” 2019; ADAPT, 2022). With a primary focus on enhancing data interoperability, ADAPT offers free accessibility. Its functionality enables seamless communication between diverse hardware and software applications, ultimately eliminating barriers to the extensive adoption of precision agricultural data.

Agrivi 360 Farm Insights (AGRIVI, 2023) was made by Agrivi as a decision support tool that enables easy recordkeeping, efficient crop planning, crop traceability, and farm administrative support. It provides a facility to manage multiple crops and multiple farms. The data can be downloaded in interoperable data formats.

Farmio (ForFarming, 2023) was developed by ForFarming, USA and it is basically an artificial intelligence-based decision support tool. It provides a module for manual on-farm activity recording and uses a smartphone app for real-time recordkeeping. This tool uses a smartphone app for real-time field data collection and provides analyzed data in an interoperable format.

Traction (Traction, 2023), made by Traction, USA is a policy and legislation support for users and a collaborative platform with John Deer Operations and Climate FieldView. This tool provides asset traceability and automatic record of activities but does not provide data to the user. Users who are using the collaboration tools can download the collected data using this software from those tools.

Agroptima (Agroptima, 2023) is usable both online and offline and provides the user a facility to store 1-month data offline and monthly cloud synchronization. It provides decision support and facilitates multiple crops and multiple farm management. It uses a mobile interface for real-time data collection.

Fieldmargine (Fieldmargin, 2023) was built by Fieldmargine, Australia and it is primarily an automatic farm condition recorder and irrigation scheduler. It offers a manual farm activity recording interface. It allows data export but in pdf format only. This tool is subscription based.

Smartphone apps in recordkeeping

The adoption of smartphones in agriculture has grown in recent years, albeit more slowly compared to other industries. Studies indicate increasing acceptance of smartphone apps and software in farm management as technology advances. For instance, Michels et al. (2020) reported a 50% adoption rate of smartphones among German farmers for farming purposes. A survey of British and French farmers found an 84% smartphone usage rate in agriculture among 57 respondents (Dehnen-Schmutz et al., 2016). The United States Department of Agriculture reported a 77% smartphone adoption rate among farmers in 2021, with over 90% using them for farming (USDA, 2021). Smartphones, equipped with various sensors and applications, offer real-time data recording and facilitate advanced farming activities cost-effectively, generating substantial data volumes (Aker & Mbiti, 2010; Jensen, 2010; Mendes et al., 2020). Smartphone apps cover diverse agricultural tasks, including asset tracking, decision support, field mapping, and financial analysis. While many focus on financial and sensor data, a few farm management apps integrate recordkeeping features. Selected popular smartphone apps based on FIMS supporting farm recordkeeping are briefly described below.

Farm Management Pro (Farm Management Pro, 2017) is a farm management tool designed for Android by SmartFarm Software, an Ireland-based company. Farm Management Pro offers recordkeeping environment for multi-crop and multi-farm with data interoperability. This 2017-released paid app does not have any web integration and was designed for keeping notes about farm activities, cost and profit, and data visualization.

Farmable: Farm Management App (Farmable, 2023) was built by Farmable, a Norway-based company in 2020. It was developed for both Android and IOS with website integration. It can be used for multiple crops and farms but does not include any animal farming module. This app offers partially automatic recordkeeping of on-farm activities as it has traceability and mobile sensor usage. It was designed for business purposes, so it works only on system subscriptions though the app is free to download. It does not provide decision support for precision agriculture but includes a financial recordkeeping facility with data export in an interoperable format.

Farm Records (FAO, 2022) is a Lebanese recordkeeping app providing an English language interface developed by Food and Agricultural Organization (FAO). This app was developed only for manual recordkeeping of farm activities and financial activities. It does not include decision support but offers multiple farms and multiple crop record facilities. The recorded data can be exported in PDF format which is hardly interoperable. The operating system for this app is Android and it was released in 2022.

BushelFarm (BushelFarm, 2022), previously known as FarmLogs, was developed by USA-based Bushel Farm Inc. in 2012. This farming tool is focused on precision farming and decision support. It has an on-farm recordkeeping module as an integration, but these data are not reported. It serves an interoperable format for the collected interoperable data. It allows manual note-keeping of farm activities with an activity scheduler and offers financial recordkeeping and decision support. This smartphone app is an integration to BushelFarms webapp.

Crop Tracker (Croptracker, 2023) was developed by CropTracker, a Canadian company in 2016. It was designed as a web application and includes smartphone apps as an integrated field companion. This app offers multi-crop and multi-farm management facilities. It is a module-based paid application, i.e., it is charged per module installation and on-farm recordkeeping is also a module that allows manual recordkeeping of farm activities. It does not allow getting data locally so it is not supporting data interoperability, but data can be exported through an on-purchase module.

xFarm (xFarm, 2023) was released in 2020 by an Italian company named xFarm. It is a digital agriculture platform for integrating IoT, decision support, management, mapping, and many more of precision agriculture. This digital agriculture tool is a web-based platform that can be used on both Android and IOS. The app is free to download but can be used on-subscribe. It offers data interoperability and allows multi-crop and multi-farm management.

Granular Insights (Corteva, 2023) is an application for Android and IOS acts as an integration of the Granular farming app. It is a web application launched by Corteva (USA) and the app was first released in 2019. The app is capable of providing a precision agriculture solution in terms of mapping fields, decision-making with data-driven insights, risk management, field planning, in-season scouting, and farm management. It offers a manual on-farm data recording module. This application is free to use but the generated data are not exportable for free.

FarmCommand (Farmers Edge, 2023), released in 2017 is a precision agriculture tool that provides decision support, satellite imagery, asset tracking, and prediction analysis developed by FarmersEdge, Canada. It contains an automated field activity recording module based on traceability and database integration. This smartphone app is an integration of a web application and can be operated on Android and IOS platforms. The app does not support open data or data interoperability.

Open source and interoperable tools are the essentials that make a smooth way for data-driven digital agriculture. To meet the demand for open-source technology in precision agriculture, FarmOS (USA) developed an open-source web application, FarmOS Field Kit (FarmOS, 2020) for farm management, planning, and recordkeeping. As a mobile integration of the FarmOS web application, FarmOS launched FarmOS Field Kit for offline field activity recording. It can be used both online and offline. FarmOS data can be exported in interoperable formats, and it is free to use.

Unstructured means of digital recordkeeping

There are some other means of recordkeeping that are being used less frequently but have potential positive impact. Text messages using texting applications such as Slack and WhatsApp are also used for farm recordkeeping (Senkomago, 2021). Slack offers channels that can facilitate note taking. Also, smartphones and tablets have note taking applications such as Sticky Notes, OneNote, Notion, which can be good platforms for notes of on-farm activities. Since smartphones have cameras and voice recorders, they can be used to improve records of an event via voice recordings, images, and videos as metadata. The unstructured aspect of these text-type tools makes retrieval of data tedious and difficult, but it is possible.

An analysis of digital systems used for farm recordkeeping

Analysis approach

This analysis encompasses computer software, web apps, and mobile apps that include a system of in-field activity recordkeeping as a focus feature or a supportive module. From the survey, 29 computer software and web applications and 31 smartphone apps having farm recordkeeping module were reviewed. These tools were evaluated based on some attributes described in Table 1. The aim of this analysis was to characterize the interfaces, services, and mode of data presentation of existing software and web and smartphone apps in farm recordkeeping.

Table 1 Aspects of computer software, web apps, and smartphone apps used for evaluation
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To analyze the categorical data, the percentage contingency (Knapp, 2015) of the variables was calculated. This assessment aimed to determine the inclusiveness of the criteria described in Table 1 and to explore the cross-association of features, particularly assessing whether tools featuring one criterion in columns includes another criterion in rows (refer to Tables 3, 5).

Analysis of computer software and web apps

The surveyed software and web apps are listed in Table 2 with their evaluated criteria with their organization and country of origin.

Table 2 Surveyed computer software and web apps with their evaluated features
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The survey result evaluating 29 farm recordkeeping software and web apps is shown in contingency analysis in Table 3. Among the software and web apps, only 17% have automated recordkeeping systems that support partially automatic data entry systems. Most of the applications include a financial recordkeeping system and decision-support modules. All of them support multi-crop management and only 3% of applications do not support multiple-farm management. More than 89% of the applications allow users to access their data but only 55% of them provide an easy interoperable/exportable data format (excluding pdf). 24% of the applications facilitate data exportation in pdf format only whereas 10% of them do not allow users to export the report (other than in image form via screen capture). 10% of the applications are open-source and 24% are free to use. Most of the open-source software and applications are free.

Table 3 Percentage contingency analysis of features in surveyed software and webapps
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Software and web apps need mobile integration for real-time recordkeeping. It is logical, therefore, that 86% of the applications use mobile interfaces as pocket data collection tools and support data accessibility from anywhere and anytime if the internet is available.

Table 3 presents the contingency analysis that provides insights into the co-occurrence of specific features in surveyed software and webapps. Each cell in the table represents the percentage of tools having a particular trait in columns that also possesses the intersecting trait in rows. This table reveals that 29% of free software and web applications provide partial support for automatic recordkeeping, with half of all freeware tools boasting mobile integration capabilities. Notably, the surveyed computer software and web applications are predominantly commercial since only 24% were freeware and 10% were open-source tools. The online-biased methodology of this survey may have led to prioritizing commercial tools. Among the open-source tools, a majority (62%) facilitate data export in interoperable file formats.

The table also shows that among the surveyed tools, all the open-source tools are freeware but not all freeware tools are open source. Only half of the freeware tools are open-source. None of the open-source software surveyed in this study offers automatic recording functionality. Open-source software developers should take note of this opportunity to improve recordkeeping. The open-source and freeware tools provide support for decision-making and financial recordkeeping (Table 3). Of apps that support automatic recordkeeping, 80% provide financial features. However, of apps supporting financial recordkeeping, only 16% support automatic or semi-automatic recordkeeping.

Analysis of smartphone apps

The smartphone application survey encompassed an examination of 31 distinct smartphone applications designed to facilitate on-farm recordkeeping. The tabulated survey results, as presented in Table 4, delineate the distribution of apps according to their operational compatibility. Notably, a percentage breakdown reveals that a minority of apps operate exclusively on the iOS platform (6.7%), a substantial fraction is exclusive to the Android operating system (23.3%), a noteworthy portion caters to both iOS and Android users (13.7%), while the majority are accessible as web applications (56.2%). The web applications occupy the majority portion of the survey because of the apps’ integration with a parent web farm management tool. Furthermore, in alignment with the evaluation process applied to online platforms and web applications, the assessment of these apps was contingent upon their adherence to the criteria delineated in Table 1.

Table 4 Surveyed smartphone apps with their evaluated features
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Table 5 shows a contingency analysis result and the percentage of specific feature-inclusive apps among the 31 surveyed apps. The analysis shows that around 23% of the apps provide automatic on-farm activity recording, especially operators’ names, activity locations, and time. Also, some advanced apps provide activity suggestions based on previous records and plans (for example, Farmable, Norway). But most of them have manual input systems. About 65% of apps provide financial recordkeeping and just over half (55%) offer decision support on either farm activity or financial management. Almost 97% of the apps provide the facility to keep activity records for multiple farms, but a few (16%) of them support crop and animal data recording in a single app. Around 68% of the apps have a data export option. Most for-fee apps allow data export. Among the apps that can export data, only 45% yield data in an easily interoperable format (not PDF). About 35% of apps are free to use.

Table 5 Percentage contingency analysis of features in surveyed smartphone apps
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The contingency analysis table for smartphone apps (Table 5) reveals that only 36% of free apps are open-source, and conversely, all open-source tools are free to use. The predominance of freeware open-source tools originated from government agencies or research centers rather than commercial entities.

A notable observation is that within the freeware and open-source categories, 9% and 0%, respectively in this survey offer automatic recordkeeping; this is a noticeable gap and a potential area for development.

The survey underscores that freeware and open-source tools exhibit proficiency in data export and interoperability, with approximately 64% of freeware and 75% of open-source tools supporting data export. Further analysis reveals that 44% and 50% of freeware and open-source tools export data in an interoperable format, respectively. Conversely, of the apps that offer data export facility, only 33% and 15% of them are freeware and open-source tools, respectively. This analysis posits a significant proportion of non-commercial applications designed with a focus on data interoperability and user accessibility. It indicates a potential avenue for non-commercial applications to enhance their integration of data interoperability features.

Additionally, a majority of the surveyed apps, whether free-to-use, open-source, or neither, offer decision support and financial recordkeeping functionalities. The percentage of apps offering decision support and financial recordkeeping functionalities is moderate across all categories.

Discussion

Current landscape of on-farm recordkeeping: opportunities and gaps

Farm recordkeeping plays a vital role in efficient farm management. Traditional paper-based methods are simple, reliable, and offer inherent provenance. However, paper records are susceptible to loss or misplacement (Quality Info Center, 2021). Despite appearing to be cost-effective initially, paper-based records entail substantial long-term costs (Kayla Ferguson, 2018). Thriemer et al. (2012) reported that paper-based approaches cost 25% more than digital methods, and data discrepancies decreased from 7 to 1% when digital platforms were used. Experiments with paper-based records may incur costs 49–62% higher than when digital approaches are used (Pavlović et al., 2009). Increased expenses in paper-based collection stem from errors during manual transcription into digital platforms (Kayla Ferguson, 2018). Lombardini and Tomkys (2015) aimed to minimize errors in recordkeeping and found that data entry is typically performed twice by distinct data-entry personnel, with a third check to identify inconsistencies. This process accounts for approximately 10% of the total data collection cost. These studies highlight the inefficiencies and challenges of using paper-based records for interoperability, data transport, model development, and analysis.

In contrast, the problem of data mobility is largely solved by digital methods. Spreadsheets and other digital tabular methods are easy to manage, and data in these formats can easily be shared and interpreted. But these tools may lack data authenticity as there is flexibility to edit the data and manipulate it. In the chronology of technology development, spreadsheets appear to be a viable alternative to paper-based methods when one considers cost, management efficacy, interoperability, and data mobility. However, as big data has emerged and enabling artificial intelligence in agriculture and farm management is a reality, there is a need for integration of different types of data from assorted sources. Data based tools are needed to enable data synchronization, remote access, and efficient data management including data security (Project Breakthrough, 2017).

The survey findings indicate that most of the software and applications in the agricultural domain are web-based, and they facilitate management across multiple users, farms, and crops. Common constraints are a paid or subscription-based user model and limited flexibility in accessing data in desired and interoperable formats. While smartphone apps serve as valuable standalone tools, they are frequently utilized as integrated extensions of their parent web apps. A limited number of tools are available as open-source and free-to-use solutions.

The technology survey has revealed a notable trend wherein only a limited number of tools emphasize or prioritize data collection within agricultural operations; those that do include data collection have a very narrow focus. Operational or field activity data provide crucial context behind production data and offer valuable insights for the development of machine learning models. Decision support tools must prioritize metadata recording to enhance the long-term value proposition in agricultural data. To that end, the survey identified that open-source and free-to-use tools have more emphasis on data interoperability and data access to users than commercial tools.

A common feature among most apps and web platforms is the provision of manual data entry modules resembling spreadsheet-style input. Unfortunately, this approach often results in data redundancy, inaccuracies, and/or missing records. Given that these applications serve multifaceted purposes, including traceability, GPS utilization, and virtual field mapping, there is an opportunity to design automated systems capable of recording activity locations, timestamps, and images. Integration of automated mechanisms that reduce human involvement in recordkeeping can be a solution to reduce missing and/or faulty data and thereby enhance data accuracy while improving the user experience.

Navigating the future: integration of advanced technologies in on-farm recordkeeping

Advancements in machine learning and AI offer opportunities to elevate recordkeeping systems to decisive or self-predictive units. Machine learning algorithms can mitigate data inconsistency and missing data in recordkeeping as natural language processing and large language models proving effective in voice-to-text translation (Gao et al., 2020; Uchihira & Yoshida, 2020). Li et al. (2021) discussed the utility and prospectus in agricultural information exchange using text-to-speech translation where deep learning can be used for information extraction. Leveraging these modern technologies can enhance the efficiency and accuracy of agricultural recordkeeping in a more effortless manner. Bunn (2020) explored AI implementation in recordkeeping for improved transparency and interdisciplinary usage. Azman et al. (2021) proposed AI implementation in automatic bookkeeping for medium and small enterprises. Cacciamani et al. (2023) suggested that models like Generative Pre-trained Transformer (GPT) and Large Language Model (LLMs) could be effective in healthcare sector recordkeeping. To facilitate interoperability, Balmos et al. (2022) discussed publisher-subscriber based atomized data transfer in IoT-based agricultural communication system, where data is transferred as bytes instead of structured file formats. Gkoulis et al. (2021) proposed an event-based microservice system to enhance interoperability in smart farming system. Although these examples were not focused on agricultural recordkeeping, they illuminate the potential for leveraging AI in agricultural recordkeeping and achieving interoperability.

Based on the discussion on technological advancement in digital on-farm recordkeeping, a visionary recordkeeping system can be proposed with automatic context recording, easy recording with natural language such as human speech, intelligent translator using ontology and data storage and data access and interoperability with proper authentication. A theoretical framework of the visionary on-farm recordkeeping system is shown in Fig. 5.

Fig. 5
figure 5

Schematic diagram of a holistic on-farm recordkeeping system

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The rapid progress of AI presents substantial growth potential for agricultural recordkeeping, but these technologies necessitate high-quality data, which is contingent on in-field recordkeeping. Consequently, manual and/or ground-truth digital recordkeeping remains indispensable for the development of advanced AI-driven recordkeeping systems.

Conclusion

The comprehensive review of contemporary on-farm recordkeeping methods reveals a rich tapestry of practices employed to date. The entire landscape of recordkeeping systems can be distinctly divided into two categories: paper-based and digital. Despite a shift toward structured digital systems, traditional paper-and-pen methods remain in common use for on-farm recordkeeping. Digital approaches, including spreadsheets, offer distinct advantages over their analog counterparts, yet there remains a critical need for data consistency and streamlined information flow. In response, innovative database-integrated forms, web apps, and smartphone apps have emerged. Smartphone apps offer more flexibility in recordkeeping. However, manual data entry remains prevalent, resulting in issues of data redundancy, inconsistency, and omission. Nevertheless, interoperability, integrity, and data plenitude challenges persist, hindering the realization of digital agriculture’s full potential. The survey highlights a substantial range of emerging open-source record-keeping tools fostering interoperability and automation. Commercial tools are increasingly providing data access in interoperable formats; but the analysis shows that open-source tools offer higher interoperability, so open-source development of data formats may facilitate better connections across all platforms. Most digital tools still require extensive manual input and are error prone; incorporation of GPTs and AI may help to reduce errors and increase data quality and consistency. Systems founded on principles of interoperable data architectures, intuitive data access, and increased automation will likely prevail.