Farm Management Information Systems Research Articles

Innovation in the Farm Office for Smart Sustainability Reporting

SummaryFarms face an increasing administrative burden due to the push for a more sustainable agri‐food system. Farms are required to demonstrate compliance with various sustainability schemes, and this trend is expected to intensify with a more performance‐based CAP and new directives and regulations. The need for smart data reporting is emphasised, with a focus on minimising the administrative burden, creating useful systems and indicators for farm management, and ensuring auditability.We advocate smart reporting as a solution and innovation in the farm office. The integration of data from Farm Management Information Systems (FMIS) with sustainability indicators is proposed in order to streamline the reporting process. The importance of using technology, such as e‐invoicing and robotic accounting, is highlighted to monitor and report crucial performance data. The integration of FMIS and Farm Financial Accounting (FFA) is proposed as a solution to enhance auditability and reduce manual input. The potential benefits of fully digital invoices and delivery notes to further streamline data exchange are proposed. Additionally, the paper discusses the role of central databases in the dairy sector.Policy implications for governments in promoting data‐intensive relationships with farmers are identified. They build upon previous policy measures that involved accounting within farms and the digital exchange of data.The paper concludes by emphasising the importance of supporting innovation in the farm office to unlock the full potential of technologies like blockchain, big data analysis, artificial intelligence and sustainability management.

Understanding Farmers' Data Collection Practices on Small-to-Medium Farms for the Design of Future Farm Management Information Systems

Farm Management Information Systems (FMIS) integrate data from a variety of sources, including sensors, for the purpose of enabling farmers to interpret past activity and predict future performance. FMIS is traditionally designed for and used by large farms, given their capital and need for automation and scale-up. This paper examines the current data collection practices on small and medium farms so that FMIS systems can be better designed to their needs. Our empirical research comprises interviews conducted during 10 farm visits. Our semi-structured interviews incorporated questions about daily activities, points of decision-making, data sharing, and incentives for data collection. We analyzed the interviews by focusing on possible obstacles to adopting expanding digital data collection practices and how expanded data collection might help fulfill farmers' goals and motivations. We found that farmers use their own bespoke data collection techniques instead of or in parallel to more formalized methods and often hold key observations and hypotheses in their heads rather than committing them to any data collection system at all. Key barriers to FMIS adoption include technology skepticism, technical hurdles, lack of support, and self-doubt in technical skills. Based on this empirical work and analysis, we recommend that FMIS systems can best address the needs of small and medium farms by 1) accounting for the farmers' different approaches to memorizing vs. storing data, 2) integrating rather than trying to replace existing practices, and 3) considering the economic and political motivations driving farm decision-making and practices.

Management Information Systems for Tree Fruit–2: Design of a Mango Harvest Forecast Engine

Spatially enabled yield forecasting is a key component of farm Management Information Systems (MISs) for broadacre grain production, enabling management decisions such as variable rate fertilization. However, such a capability has been lacking for soft (fleshy)-tree-fruit harvest load, with relevant tools for automated assessment having been developed only recently. Such tools include improved estimates of the heat units required for fruit maturation and in-field machine vision for flower and fruit count and fruit sizing. Feedback on the need for and issues in forecasting were documented. A mango ‘harvest forecast engine’ was designed for the forecasting of harvest timing and fruit load, to aid harvest management. Inputs include 15 min interval temperature data per orchard block, weekly manual or machine-vision-derived estimates of flowering, and preharvest manual or machine-vision-derived estimates of fruit load on an orchard block level across the farm. Outputs include predicted optimal harvest time and fruit load, on a per block and per week basis, to inform harvest scheduling. Use cases are provided, including forecast of the order of harvest of blocks within the orchard, management of harvest windows to match harvesting resources such as staff availability, and within block spatial allocation of resources, such as adequate placement of harvest field bin and frost fans. Design requirements for an effective harvest MIS software artefact incorporating the forecast engine are documented, including an integrated database supporting spatial query, data analysis, processing and mapping, an integrated geospatial database for managing of large spatial–temporal datasets, and use of dynamic web map services to enable rapid visualization of large datasets.

Integrated Route-Planning System for Agricultural Robots

Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs’ performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation.

Management Information Systems for Tree Fruit—1: A Review

A farm management information system (MIS) entails record keeping based on a database management system, typically using a client-server architecture, i.e., an information system, IS, coupled with a variety of tools/methods/models for the support of operational management. The current review adopts a multivocal approach to consider academic and commercial developments in MISs for orchard management, based primarily on the refereed literature but extending to grey literature and interviews of Australian mango orchard managers. Drivers for orchard MIS development include increasing the orchard size and management complexity, including regulatory requirements around labour, chemical spray use and fertilisation. The enablers include improvements in within-orchard communications, distributed (web) delivery systems using desktop and mobile devices, and sensor systems and predictive models, e.g., for pest management. Most orchard MIS-related publications target the commodities of apple, grape, mango and olive in the context of management of plant health (pest and disease), plant development, irrigation and labour management. Harvest forecast and MIS modules are only now beginning to emerge, in contrast to a long history of use in grain production. The commercial systems trend towards an incorporation of financial information, an integration of data from multiple sources and a provision of dashboards that are tailored to the user. Requirements for industry adoption of a MIS are discussed in terms of technical and design features, with a focus on usability and scalability.

Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study

A web mapping XYZ Tile Layer Service, such as Google Earth (GE), provides an amazing resource for the visualization of spatial data against aerial and satellite imagery with global coverage, typically at a resolution finer than 5 m. However, the increasing requirement on spatial accuracy in farm information requires a greater appreciation of the issues involved in the use of such services. Position errors can be created in the georeferencing and orthorectification of images, transformation between reference frames (datums) in map projection, e.g., using a spheroid as compared to an ellipsoid earth model, and tectonic shifts. A review is provided of these issues, and a case study is provided of the horizontal positional accuracy of web map imagery for Australian mango orchards. Positional accuracies varied from 1.804 to 6.131 m across four farms using GE 2021 imagery, between 1.556 and 3.365 m in one farm for the most recent imagery available from each of four web map providers, and from 0.806 m (in 2016) to 10.634 m (in 2003) in one farm for the period of 2003 and 2021 using the historical GE imagery resource. A procedure involving the estimation of four transformation parameters was demonstrated for the alignment of GNSS data with GE imagery. However, as the scale factor was unity and the rotational value was near zero, the use of a simple horizontal mean shift vector was recommended. Further recommendations are provided on (i) the use of web mapping services, with a comparison of the use of UAV survey imagery, and (ii) the need for metadata, particularly the date of collection, on collected position data, in the context of use in farm management information systems.

Proposed Farm Information Management System Design and Database Management for Recommendation Fertilizer and Financial Record Feature

This study proposes a comprehensive Farm Information Management System (FIMS) design and database management strategy for addressing the specific requirements of Indonesian agriculture, with a particular emphasis on fertilizer recommendation and financial record management. The study employs the Software Development Life Cycle (SDLC) methodology as a framework for directing the system’s design, development, and implementation. The goal of the FIMS is to provide producers with a user-friendly interface to access accurate fertilizer recommendations based on crop-specific requirements and local soil conditions. Additionally, the system includes a robust financial record function to aid producers in effectively managing their income, expenses, and investments. The proposed SDLC-based approach assures a structured and methodical development process, including requirements analysis, system design, implementation, testing, and maintenance. Using the SDLC methodology, this study seeks to develop a scalable, secure, and interoperable FIMS that can be effectively integrated into Indonesia’s existing agricultural infrastructure. The effectiveness and usability of the proposed system will be determined through extensive testing and evaluation to ensure its suitability and functionality in actual agricultural contexts. Ultimately, the proposed Farm Information Management System, developed using the SDLC method, aims to empower Indonesian farmers with optimized fertilizer recommendations and streamlined financial record management, thereby contributing to sustainable agriculture practices, and enhanced economic incomes for the nations.

Digital transformation of agriculture: A new integrated modeling framework for arable farm enterprises

With the increasing integration of information technology in arable farms, enterprise integration (EI) is becoming increasingly important. As farms handle many applications, all of which work in isolation from one another, integrating these disparate systems to create unity across the enterprise has clear value. One critical aspect of EI is business integration, which helps the enterprise align the use of technology assets with the business to achieve strategic goals. While existing enterprise modeling (EM) methods are often used to model industrial or general enterprises for business integration, they do not fully meet the needs of arable farm enterprises. This study aims to propose an EM framework for arable farm enterprises that provides an effective and practical method for business integration. Therefore, an integrated EM framework for arable farm enterprises (IEMFAFE) is proposed using a domain analysis approach based on EM theory. Additionally, steps and methods for modeling arable farm enterprises based on IEMFAFE are provided. A multi-case study shows that IEMFAFE is well suited for modeling arable farm enterprises. The high expressiveness of IEMFAFE enables accurate descriptions of enterprise details and extraction of the service modules and information entities necessary for system design, significantly improving the speed and quality of the system design. The contributions of this study will benefit both researchers investigating the integration of arable farm enterprises and practitioners aiming to architect integrated farm management information systems.

Using an open source and resilient technology framework to generate and execute prescription maps for site-specific manure application

The development of precision farming solutions and the required network infrastructure appear to be progressing at different speeds and levels of deployment. In most of the world's rural areas, where farming takes place, network coverage and infrastructure is at a basic level. However, where digital farming solutions are applied, internet-dependent applications need backup solutions to enable the same or a comparable performance towards maintaining the functionality of the specific practice and ultimately food production. The aim is to avoid regressing to basic practices waiving advantages of established precision farming technologies. Based on a conceptual framework, defined by the authors in a previous work, site-specific slurry application was chosen as a concrete use case to implement and demonstrate fallback options to maintain efficient and reliable application even during internet outages. Initially, the differences between conventional and resilient IT infrastructure were contrasted and investigated. The generation of a prescription map (PM) based on the fusion of multiple parameters, clustered in the domains of soil, yield, and remote sensing, was performed utilizing open-source and offline usable software. For seamless transmission of the generated PMs, a new and innovative hardware platform was integrated into the machinery fleet and working process of a machinery ring (MR), which was acting as a service contractor in the inter-farm slurry application. Interoperability was considered by enabling the executing software to accept PMs of altering origin. The on- and off-line generation of PMs including data fusion, were also compared. Here the multi-parametric approach corresponded to the ground truth data, whereas the PMs generated in an online farm management information system (FMIS), using only one data source, showed deviations in management zone patterns and relative dose rates. In addition, the impact of the lack of online accessible data on the delineation of management zones was investigated and resulted in a switch from the medium to the high dose rate (DR) level in 9.1% of the field's area.

Adoption of farm management information systems (FMIS): The case of Brazilian sugarcane farmers

Farm Management Information Systems (FMIS) have been adopted by farmers for planning, monitoring, and controlling agricultural operations. These systems are at the core of the digital farming revolution and can improve the technical, economic, and environmental efficiency of farming. This study evaluates the factors that influence the decision to adopt FMIS by sugarcane farmers in the state of São Paulo, Brazil. A survey of 131 sugarcane farmers provided cross-sectional farm-level data for the 2018/19 crop year, which were analyzed by a logit regression model. The decision to adopt FMIS is positively affected by farmers’ level of education, private advisor, access to rural credit, off-farm income, farm size, and use of forward contracts. FMIS adoption is associated with risk-averse farmers as part of a risk management strategy. The results can help farmers in their decisions to adopt and both policymakers and FMIS suppliers in their strategies to foster FMIS diffusion. FMIS diffusion can increase sugarcane production efficiency resulting in positive shocks to sugar and ethanol production, which are key components of Brazil's rural development and energy strategies.

Agricultural process data as a source for knowledge: Perspective on artificial intelligence

In visions of the future of agriculture, it is predicted that Artificial Intelligence and Robotics will revolutionize farming. Artificial Intelligence (AI) is not always clearly visible to the end users, who are in this case farmers. AI methods are usually incorporated in existing Farm Management Information Systems or in some cases in separate Decision Support Systems; or the AI is part of the machine or robot operation. Currently, AI methods are used mostly in machine vision applications. Practical applications that use methods other than image data are scarce. However, agricultural machines produce an ever-increasing amount of process data during agricultural operations. There is huge potential to gain useful information from this data. This paper presents a case example of treatment-zone determination based on agricultural process data using clustering methods. This paper also describes how AI is incorporated into the Cropinfra research data collection platform. Based on the experiences gained from Cropinfra-related research projects and a review of research papers, we predict the future directions of Artificial Intelligence in agriculture.

Mind the Market Opportunity: Digital Energy Management Services for German Dairy Farmers

The adoption of farm management information systems (FMIS) is on the rise at German dairy farms given their benefits in supporting and automating decision-making processes. However, the offering scope of FMIS for dairy farmers is limited, with digital services mostly focusing on animal-related data and overall economic insights. By contrast, digital energy management services (DEMS) are not yet established as an integral part of FMIS despite their expected positive contribution to a dairy farm’s ecological sustainability and profitability. Against this background, the aim of this study was to find out if there is a hitherto undetected market opportunity for FMIS providers offering DEMS to German dairy farmers. To achieve this aim, the as-is market offering was screened looking at seven pre-defined DEMS, and customer preferences were investigated based on online survey responses from 74 German dairy farmers. Results of the survey indicate a high relevance of DEMS, which especially applies for optimization-oriented energy data analyses. The market coverage of such digital services, on the other hand, is not yet adequate. Hence, for providers of FMIS, we see a promising market opportunity to expand their offering by starting to deploy selected DEMS to German dairy farmers.

Integration of Farm Financial Accounting and Farm Management Information Systems for Better Sustainability Reporting

Farmers face an increasing administrative burden as agricultural policies and certification systems of trade partners ask for more sustainability reporting. Several indicator frameworks have been developed to measure sustainability, but they often lack empirical operationalization and are not always measured at the farm level. The research gap we address in this paper is the empirical link between the data needs for sustainability reporting and the developments in data management at the farm level. Family farms do not collect much data for internal management, but external demand for sustainability data can partly be fulfilled by reorganizing data management in the farm office. The Farm Financial Accounts (FFAs) and Farm Management Information Systems (FMISs) are the main data sources in the farm office. They originate from the same source of note-taking by farmers but became separated when formalized and computerized. Nearly all European farms have a bank account and must keep financial accounts (e.g., for Value-Added Tax or income tax) that can be audited. Financial accounts are not designed for environmental accounting or calculating sustainability metrics but provide a wealth of information to make assessments on these subjects. FMISs are much less frequently used but collect more technical and fine-grained data at crop or enterprise level for different fields. FMISs are also strong in integrating sensor and satellite data. Integrating data availability and workflows of FFAs and FMISs makes sustainability reporting less cumbersome regarding data entry and adds valuable data to environmental accounts. This paper applies a design science approach to design an artifact, a dashboard for sustainability reporting based on the integration of information flows from farm financial accounting systems and farm management information systems. The design developed in this paper illustrates that if invoices were digitized, most data-gathering needed for external sustainability reporting would automatically be done when the invoices is paid by a bank transfer. Data on the use of inputs and production could be added with procedures as in current FMISs, but with less data entry, fewer risks of differences in outcomes, and possibilities of cross-checking the results.

GeoBox: design and evaluation of a tool for resilient and decentralised data management in agriculture

ABSTRACT Farm Management Information Systems (FMIS) are an important core component of modern farming companies as they allow, e.g. to document activities, create fertilisation plans, and feed digital equipment with required data. Since the entire agricultural sector is an essential component of food production, high standards of resilience should be established in the involved companies. Accordingly, the used software should also be designed with high standards on reliability and crisis capability. Based on a literature review, we found that software for farmers with certain resilience needs is lacking. Thus, we designed and evaluated a new FMIS concept with the user-centred design method. By conducting focus groups (two rounds, total N = 57) in 2017 and 2019, we raised specific front-end and back-end requirements of farmers. Based on the requirements, we developed our concept for both front- and back-end in terms of a decentralised and offline-working FMIS. Through the evaluation with practitioners (N = 16) of the implemented concept, we derived findings and implications, highlighting the need for privacy, stability, and offline-capability, as well as the UI-requirement to be supportive, e.g. with easy to understand icons and terms.

Commercial farm management information systems - A demand-oriented analysis of functions in practical use

The functions of farm management information systems (FMIS) are mostly examined without considering purchasing statistics as an indicator for their actual use and crucial information for FMIS developers and politics. Thus, the present study analyzes categories and functions of commercial FMIS purchased by farmers within a subsidy program for digital technologies in the German federal state of Bavaria. The FMIS were categorized and linked to eleven general functions. Correlations between supplied FMIS functions and farmers’ purchase decisions were evaluated. Finally, the distribution of FMIS functions and categories were weighted by the number of purchases to reflect their demand. The distribution of categories and functions were also compared to previous studies to show current trends. From October 2018 to June 2020, 52 different FMIS were purchased 569 times, dominated by ten FMIS covering 85 % of purchases. The most purchased FMIS targeted the crop domain. Farmers tend to use office and mobile software versions in combination more often. Although web applications seem to increase, native applications, developed for use on a particular platform or device, were still the most inquired application type. There is evidence that certain functions increase farmers’ willingness to use FMIS when they are available. The most purchased FMIS functions were ‘quality assurance’, ‘inventory’ and ‘finance’. The availability of the functions ‘traceability’ and ‘quality assurance’ clearly increased, confirming a positive trend already predicted in a previous study in 2015. In conclusion, functions providing an automated and site-specific mapping, monitoring, and recording of farm processes and production materials to comply with legislative standards were highly requested by farmers, presumably due to increasing cross-compliance requirements of the Common Agricultural Policy of the European Union. Future FMIS should therefore include functions for recording and evaluating site-specific agri-environmental measures to support result-based payments and related decision-making.

Embedding a precision agriculture service into a farm management information system - ifarma/PreFer

Today, bridging precision agriculture (PA) with farm management is a high priority. To respond to this challenge, a precision fertilization service for extended crops, namely ‘PreFer’ (developed by 'Ecodevelopment' enterprise), was embedded as a new module in a cloud-based farm management information system (FMIS), namely ‘ifarma’ (developed by 'Agrostis' enterprise). The new PreFer module preserves the full potential of the original service methodology, while taking advantage of all fundamental functionalities of the ifarma platform. PreFer as a service uses a GIS to store and process the farmers’ geodatabases, which are fed from multiple sources, such as soil surveys, satellite data, yield monitors, etc. This GIS is also used to feed the machine learning algorithms of ‘PreFer’ with the required data to produce the prescription maps for both broadcasting and topdressing fertilizations. Then, all tables and maps are transferred from the GIS to the platform upon their production, thus becoming immediately available to the farmers. The ‘ifarma/PreFer' module was tested during the 2022 cultivation season, showing that fully meets farmers' requirements. This work also indicates that synergies like this are more than necessary to create added-value in commercial precision agriculture.

Towards an Architecture for Online Scheduling of Autonomous Robots in Agriculture

Nowadays, we observe the development of autonomous robots for agricultural tasks. Farmers are becoming task and data managers with the emergence of advanced farm management information systems (FMISs). However, existing FMISs lack the tools for handling scheduling and monitoring of fleets of robots. The scheduling functionalities are essential for the growth of autonomous robot industry. It allows a better management to share these state of the art and expensive resources between multiple farmers, reducing the overall cost. Scheduling is always coupled with a re-scheduling process that allows to react to unexpected events. The re-scheduling process, called online scheduling, can only be made possible with a monitoring process that collects real-time information about the ongoing tasks and the state of robots. Finally, relatively little is known about the changes in farmers' activities as a result of the introduction of these robots. Acceptance of these new technologies is nevertheless essential to the performance of the systems. Motivated by the lack of a general framework for the online scheduling of autonomous robots for agriculture, the authors propose a conceptual framework for the scheduling and monitoring of such shared resources. All the needed building blocks for the whole conceptual framework to function efficiently are highlighted. Open issues related to each of these building blocks are discussed, from robotics auto-diagnosis to data management, wireless communication, scheduling, monitoring, and controlling these autonomous robots, keeping in the loop the human operator and his essential role in this system.

A Contextualized Farm Management Information System

Various Information Communication Technology (ICT) Agricultural initiatives implemented in Kenya have largely concentrated on the general farming set ups, ignoring the specific farm and Agricultural activities contexts. Literature review, sampling of ICT for Agriculture initiative (ICT4Agri) reports, and our knowledge on existing Agricultural Management Information Systems (AMISs) showed that a majority of the ICT4Agri for various farm management were general in nature and mostly facilitated information push towards farmers as opposed to allowing for a bidirectional flow of information. Only a few personalized and contextualized MISs for day-to-day management of farms existed. Besides their abundance, adoption, and use, challenges still exist due to the generalized nature in which system designers and developers conceptualize the ICT4Agri artefacts. With the objective of finding out if a more contextualized Agricultural farm Management Information System was better than the generalized Agricultural systems, we carried out an analysis of the current situation and designed, implemented, and tested a new integrated ICT4Agri dairy farm Management Information System. The system was co-designed with five purposefully selected ICT literate dairy farmers from the North rift counties of Kenya. Besides the presentation of the developed artefact, this paper presents the study results which showed that there is still need for compatible ICT agricultural systems which can allow farmers to process their own data by being allowed to have bidirectional movement of data and being able to create and use their own data. As a result, it was noted that there still exists a lot of opportunities for Agricultural digitization, especially if contextualization was to be factored in

Exploring information uses for the successful implementation of farm management information system: A case study on a paddy rice farm enterprise in Japan

Recently, the development of farm management information systems (FMIS) has increased. Although many argue that the use of FMIS can enhance decision making, we still do not fully understand the impact of these systems on the individual and farm levels. Thus, we conducted an eight-month investigation and in-depth interviews with one of the leading paddy rice farm practicing data-driven management with intensive using FMIS in Japan. This study found that information use varies across farming operations activities, and by managers and employees. In addition, the system quality and information quality of FMIS associate with information use. The use of FMIS and information facilitated members in sharing information formally and informally, thereby increasing decision making efficiency and informal mutual adjustment among employees. Likewise, the participative management of employees emerged. We then discuss how the FMIS-enabling information usage might facilitate the complex organizational structure.

How to encourage farmers to digitize? A study on user typologies and motivations of farm management information systems

Farm Management Information Systems (FMIS) have undergone tremendous development in the recent past. With the sharp increase in documentation requirements for agricultural businesses, the potential of FMIS to become widely used comprehensive management tools is high but not yet fully exploited in practice. Consequently, there is a need to improve and adjust these systems. However, there is little research about how to optimize FMIS in order to meet the needs of the users. To answer this question, a deeper understanding of how users differ from other groups regarding their intensity of use and their perceptions of FMIS is needed but has hardly been explored so far. By means of a standardized online questionnaire in the summer of 2019, the skills and attitudes of 280 German farmers regarding the use of FMIS were surveyed. By means of a cluster analysis, different user segments were analyzed to gain deeper insights into which characteristics FMIS users and non-users have. Four clusters were identified including two non-user groups and two user groups with varying potential for FMIS providers and marketers. The results show that the lack of persuasiveness of the existing systems by not (yet) offering solutions to farmers’ problems and needs is a major barrier. One main driver for the use of FMIS is to manage the increasing political and social requirements with the help of a professional documentation of farm activities. On the one hand, the results accompany the ongoing digital transformation in agribusiness and contribute on the other hand to the digitization process on farms by increasing new knowledge about farmers’ requirements regarding FMIS. The results also provide relevant insights for software development companies to adjust their products according to the farmers’ needs.