Farm Record Keeping: Essential Templates and Software for Better Management

Farm record keeping is the systematic capture of production, financial, health, and management data that supports daily decisions and long-term planning. For farmers, employees, veterinarians, advisers, and students, the choice between paper ledgers, spreadsheets, and specialized software depends on farm size, enterprise type, labor structure, and the specific decisions that records must inform. This article compares record keeping methods, provides practical templates for finances, livestock, and crops, and outlines implementation steps that work across different farming systems.

Why Farm Records Matter for Management Decisions

Records transform observations into information that can guide action. Without accurate records, a farmer cannot know whether a treatment protocol is working, whether a feed change improved growth, or whether a particular crop enterprise is profitable. The value of records lies not in the act of writing but in the analysis that follows.

Computerized systems have demonstrated this value across species and regions. In dairy operations, computerization allows veterinarians providing reproductive herd health services to become a vital part of the record keeping and evaluation systems of the farms in their practices [6]. This integration means that health professionals can interpret farm data and recommend changes based on patterns instead of memory.

The same principle applies to sheep and goat production. Managing a milk zone in the dairy industry is demanding, and data necessary for efficient management are difficult to acquire because they usually must be collected in organized and standardized ways [7]. Software practices constantly provide new tools that can go beyond simple record-keeping practices and add value to the data [7]. When records are standardized, they become comparable across time periods and across animals, which is the foundation of sound culling, breeding, and feeding decisions.

For breeding programs specifically, a fundamental requirement is the availability of accurate and reliable pedigreed data and tools facilitating sophisticated computations [8]. Without pedigree records, genetic improvement is guesswork. With them, farmers can rank animals, calculate breeding values, and make selection decisions that improve herd or flock performance over generations.

At a Glance: Comparing Record Keeping Methods

The comparison above reflects common tradeoffs instead of universal rules. A small farm with a single enterprise may find paper records perfectly adequate. A large operation with multiple species, employees, and regulatory requirements will likely need software to manage the volume and complexity of data.

Core Principles of Effective Record Keeping

Standardization Before Digitization

The most important principle is that records must be collected in organized and standardized ways [7]. Standardization means defining what data to collect, how to name items, and when to record them before choosing a tool. A spreadsheet with inconsistent entries is no better than a paper ledger with gaps.

Standardization applies to:

• Animal identification (ear tag numbers, names, or registration numbers)
• Date formats
• Treatment names and dosages
• Feed types and quantities
• Cost categories
• Crop field names and block numbers

Data Quality Over Data Quantity

Manual record keeping has not been able to make an impact on management due to difficulties experienced in quality control of the data and in analyzing the data to produce useful information for farm managers [9]. This observation from dairy extension work in Malaysia applies broadly. Collecting too many data points that are never analyzed wastes time and discourages consistent recording.

Quality control means checking that entries are complete, legible, and accurate at the time of recording. It is easier to fix an error on the day it happens than to reconstruct missing information weeks later.

Records Located Where Work Happens

The physical location of records affects whether they get completed. In a study of calf health recording on dairy farms in Ontario, producers had greater odds of recording anti-inflammatory treatments if records were located in the calf barn than elsewhere [10]. When calf health records were kept in the calf barn, respondents were less likely to report that illnesses were not recorded due to time constraints [10].

This finding supports a simple design principle: put the record where the work occurs. A clipboard in the barn, a waterproof notebook in the field, or a mobile app on a phone carried by the stockperson will capture more data than a computer in the farm office.

Records as a Tool for Employees

Record keeping is also a management task. Nonfamily employees had greater odds of recording all supportive therapy treatments than farm owners in the Ontario calf health study [10]. This suggests that employees can be reliable record keepers when the system is clear and accessible.

Training employees on what to record, how to record it, and why it matters improves compliance. A record system that employees understand and find useful is more likely to be maintained than one imposed without explanation.

Practical Workflow for Implementing a Record Keeping System

Step 1: Define the Decisions You Need to Make

Start by listing the recurring decisions on your farm. These might include:

• Which animals to cull or retain for breeding
• Whether a treatment protocol is working
• Which feed ration is most cost effective
• Which fields or crops are profitable
• When to market animals or crops
• Whether to expand or reduce an enterprise

Each decision implies specific data requirements. Culling decisions require production, health, and reproductive records. Feed decisions require intake, weight gain, and cost data. Marketing decisions require growth rates and market price information.

Step 2: Choose the Minimum Data Set

For each decision, identify the smallest set of records that will support it. A minimum data set for a cow-calf operation might include:

• Calving date and ease
• Calf birth weight and sex
• Weaning weight
• Dam and sire identification
• Health treatments with dates and products
• Sale weight and price

For a crop enterprise, the minimum data set might include:

• Field or block identification
• Planting date and variety
• Input applications (fertilizer, seed, chemicals) with dates and rates
• Harvest date and yield
• Sale price and total revenue

Step 3: Select the Recording Tool

Match the tool to the farm's size, labor, and technical capacity. A farm with no reliable computer access should not adopt cloud-based software. A farm with multiple employees and high data volume should not rely on a single paper ledger.

Consider the following when selecting software:

• Does it work offline or in low connectivity areas?
• Can multiple users enter data?
• Can data be exported for analysis or reporting?
• Does the vendor provide training and support?
• What is the total cost including subscriptions, hardware, and training time?

Step 4: Design Forms and Templates

Whether using paper or software, forms should be simple and field-tested. A good form has:

• Clear headings for each data item
• Consistent order matching the workflow
• Enough space for legible entries
• Pre-printed options where possible (checkboxes, dropdown menus)

Step 5: Train Everyone Who Records Data

Training should cover what to record, when to record it, and how to handle missing or uncertain information. Training should also explain how the records will be used, because people record more carefully when they understand the purpose.

Step 6: Schedule Regular Analysis

Records have no value until they are analyzed. Schedule a regular time, weekly or monthly, to review records and extract decisions. This analysis can be simple: calculating average daily gain, comparing treatment success rates, or totaling monthly feed costs.

Step 7: Review and Adjust the System

After a season or a production cycle, review the record system itself. Are the forms capturing the right data? Are entries complete? Is the analysis producing useful information? Adjust the system based on these observations.

Financial Record Keeping Templates

Cash Flow and Income Tracking

A cash flow record tracks money coming in and going out on a daily or weekly basis. The essential columns are:

• Date
• Description or source
• Income amount
• Expense amount
• Category (feed, health, labor, fuel, marketing, etc.)
• Running balance

This record supports short-term decisions about purchasing inputs, timing sales, and managing credit. It also provides the raw data for annual profit calculations.

Enterprise Cost of Production

Cost of production records allocate expenses to specific enterprises or enterprises within a farm. For example, a mixed farm might track the costs of the beef enterprise separately from the crop enterprise. The essential data are:

• Revenue by enterprise
• Direct costs (feed, health, seed, fertilizer, fuel)
• Allocated overhead costs (labor, machinery, utilities)
• Production volume (kilograms of meat, liters of milk, tonnes of grain)

This record answers the question of which enterprises are profitable and which are not. It supports decisions about resource allocation and enterprise mix.

Inventory and Asset Records

Inventory records track inputs and outputs that are stored on the farm. These include:

• Feed inventory (type, quantity, purchase date, cost)
• Medicine and vaccine inventory (product, lot number, expiry date, quantity)
• Seed and fertilizer inventory
• Livestock inventory (number by class, age, and location)
• Machinery and equipment inventory (purchase date, cost, maintenance history)

Inventory records prevent overbuying, support biosecurity through lot tracking, and provide the data needed for insurance and tax purposes.

Livestock Record Keeping Templates

Individual Animal Records

For breeding stock and dairy animals, individual records are essential. The core fields are:

• Animal identification (ear tag, tattoo, or registration number)
• Breed and birth date
• Sire and dam identification
• Purchase or birth origin
• Health events with dates and treatments
• Reproductive events (breeding, calving, weaning dates)
• Production data (milk yield, weight, growth rate)
• Disposal date and reason (sold, culled, died)

Individual records support culling decisions, breeding value estimation, and health management. They also provide the pedigree data that is fundamental for genetic improvement [8].

Herd or Flock Health Records

Health records can be kept at the group level for many purposes. The essential fields are:

• Date of observation
• Group or pen identification
• Number of animals affected
• Clinical signs observed
• Treatment administered (product, dose, route, withdrawal period)
• Outcome (recovered, died, culled)
• Veterinarian involvement

Accurate illness and treatment rates are crucial for establishing disease prevalence and evaluating control programs [10]. Incomplete health records make it impossible to know whether a disease problem is improving or worsening.

Treatment and Withdrawal Records

For food-producing animals, treatment records must include enough information to ensure that withdrawal periods are respected. The essential fields are:

• Animal or group identification
• Date of treatment
• Product name and batch or lot number
• Dose and route of administration
• Withdrawal period for meat or milk
• Date when withdrawal period ends
• Person who administered the treatment

These records protect food safety and support responsible antimicrobial use. Regulatory authorities and veterinary advisers rely on treatment records to assess on-farm practices.

Breeding and Reproduction Records

Reproduction records track the events that determine herd or flock productivity. The essential fields are:

• Animal identification
• Breeding date and method (natural service, artificial insemination)
• Sire identification
• Pregnancy diagnosis date and result
• Calving or lambing date
• Number of offspring and their identification
• Dystocia or other calving problems

Reproductive records support fertility management, which is central to the economics of dairying [9]. They also provide the data needed for genetic evaluation and culling decisions.

Crop Record Keeping Templates

Field History and Planting Records

Field history records track what has happened in each field or block over time. The essential fields are:

• Field or block identification
• Crop and variety
• Planting date and seeding rate
• Soil test results
• Fertilizer applications (product, rate, date)
• Pesticide applications (product, rate, date, target pest)
• Irrigation events
• Harvest date and yield

Field history supports crop rotation planning, input budgeting, and the evaluation of management practices across seasons.

Input Application and Cost Records

Input records track the products applied to crops and their costs. The essential fields are:

• Date of application
• Field or block identification
• Product name
• Application rate
• Area treated
• Total product used
• Cost per unit and total cost

These records support cost of production calculations and provide the documentation needed for compliance with input use regulations.

Yield and Marketing Records

Yield records track production and its value. The essential fields are:

• Field or block identification
• Harvest date
• Yield by weight or volume
• Moisture content if relevant
• Grade or quality classification
• Buyer and sale price
• Total revenue

Yield records combined with input records produce the cost and return data that determine enterprise profitability.

Software Options for Farm Record Keeping

General Purpose Spreadsheets

Spreadsheets such as Microsoft Excel or Google Sheets are flexible tools that can be adapted to almost any farm record need. They require no special software purchase, and most farmers have some familiarity with basic functions.

The strengths of spreadsheets include:

• Low cost
• Full control over form design
• Ability to create custom calculations
• Data export to other programs

The limitations include:

• No built-in data validation unless programmed
• Version control problems when multiple people edit files
• Limited mobile access unless using cloud versions
• No automatic backups unless configured

Specialized Farm Management Software

Specialized software is designed for specific enterprises or species. Examples include dairy herd management programs, beef cattle record systems, sheep and goat management applications, and crop management platforms.

The strengths of specialized software include:

• Built-in data structures for the enterprise
• Automated calculations and reports
• Integration with milk recording, weighing, or other data collection systems
• Support from vendors and user communities

The limitations include:

• Purchase and subscription costs
• Training requirements
• Data entry commitment
• Potential difficulty transferring data if switching programs

Mobile Applications

Mobile applications allow data entry at the point of work, which improves recording compliance [10]. Applications range from simple note-taking tools to comprehensive farm management platforms.

The strengths of mobile applications include:

• Immediate data entry at the animal or field
• Photo and voice capture options
• Offline functionality in many cases
• Low cost for basic versions

The limitations include:

• Small screens for complex data entry
• Battery and connectivity issues
• Data export limitations in some apps
• Device durability in farm conditions

Decision Support Systems

Some software goes beyond record keeping to provide decision support. These systems use farm data to generate recommendations or predictions. For example, a decision support system for sheep and goat production can model data collection processes and provide a straightforward user interface to facilitate data processing and visualization [7]. In a case study, using such an app resulted in lower feeding cost per milked ewe when ewes were allocated into high and low milk production groups compared to remaining in one single group [7].

Decision support systems can also support breeding decisions. One system developed for sheep management is capable of automatic performance recording, farm data management, data mining, biometrical analysis, and decision-making [8]. It calculates breeding values, inbreeding coefficients, and selection indices, and generates more than 40 types of custom-tailored animal and farm reports [8].

These tools are most valuable when the farm has sufficient data volume and management complexity to justify the learning investment.

Records and Measurements That Drive Decisions

Production Measurements

Production records only have value when they are measured consistently. Key measurements include:

• Weight at defined ages (birth, weaning, yearling, market)
• Milk yield per day or per lactation
• Egg production per hen per week
• Crop yield per hectare or acre
• Feed conversion ratio (feed consumed divided by weight gain)

Consistent measurement protocols are essential. Weighing animals at the same time of day, using the same scales, and recording under the same conditions reduces variation that is not related to real performance differences.

Health and Treatment Measurements

Health records support the calculation of:

• Disease incidence (new cases in a period divided by animals at risk)
• Treatment success rate (animals recovered divided by animals treated)
• Mortality rate (animals died divided by animals in the group)
• Culling rate and reasons

These measurements allow farmers to evaluate whether health interventions are working and whether disease patterns are changing over time.

Financial Measurements

Financial records support the calculation of:

• Gross margin per animal or per hectare
• Cost per unit of production (per liter of milk, per kilogram of gain, per tonne of grain)
• Break-even price
• Return on investment for specific enterprises

These measurements require accurate cost allocation. Overhead costs such as labor, machinery, and utilities must be assigned to enterprises in a consistent way.

Common Failure Patterns in Farm Record Keeping

Inconsistent Data Entry

The most common failure is inconsistency. Dates recorded in different formats, animals identified by different names, and treatments recorded without doses all reduce the value of records. Inconsistency makes analysis difficult and undermines confidence in the data.

Prevention requires standardization at the form design stage and training for everyone who enters data. Regular checks of data quality can catch problems before they become habits.

Records Collected But Never Analyzed

Many farms collect records diligently but never use them for decisions. This pattern was observed in the Ontario calf health study, where some respondents reported that calf health records were not analyzed [10]. Records that are not analyzed have no value, and farmers eventually stop recording when they see no benefit.

Prevention requires scheduling regular analysis time and connecting specific records to specific decisions. If a record does not support a decision, it should be eliminated.

Tools That Do Not Match the Farm Context

A record system that works on a large commercial dairy may fail on a smallholder farm with limited connectivity and technical support. The barriers to effective record keeping include inaccessibility of digital tools and lack of knowledge or skills [25]. These barriers are more important than farmer attitudes in many cases.

Prevention requires selecting tools that match the farm's actual conditions, including connectivity, electricity, technical skills, and labor availability.

Single Point of Failure

A paper ledger kept in the farm office is lost if the office floods or burns. A spreadsheet stored on one computer is lost if the hard drive fails. A cloud-based system is lost if the subscription lapses and data export was never performed.

Prevention requires backups. Paper records should be copied or photographed. Digital records should be backed up automatically or on a regular schedule.

Overcomplicated Systems

Systems that require too many data fields or too much time per entry will be abandoned. The Ontario calf health study found that time constraints were a reported reason for not recording illnesses when records were kept away from the calf barn [10]. Simplicity is a feature, not a weakness.

Prevention requires designing the minimum data set and resisting the urge to add fields that will never be used.

Limitations of Record Keeping Systems

Data Quality Depends on Human Behavior

No software can fix inaccurate data entry. If the person recording the data does not know the animal's identity, the treatment given, or the date of the event, the record will be wrong regardless of the tool used. Training and supervision are essential components of any record system.

Records Describe the Past, Not the Future

Records tell you what happened, not what will happen. They support decisions by providing evidence about past performance, but they cannot predict market prices, weather, or disease outbreaks. Farmers must combine records with current observations and external information.

Analysis Requires Skills

Collecting data is easier than analyzing it. Many farmers have the discipline to record but lack the statistical or financial skills to extract useful information. Advisers, veterinarians, and extension services can help bridge this gap.

Technology Can Create New Barriers

Digital tools can reduce recording effort, but they can also create new barriers. Connectivity problems, device failures, software updates, and data migration issues can interrupt recording and erode confidence in the system. A backup paper system is a practical safeguard.

Welfare and Safety Context for Record Keeping

Animal Welfare Monitoring

Records support animal welfare by documenting health events, treatments, and outcomes. They allow farmers to detect problems early, evaluate the effectiveness of interventions, and demonstrate care to customers and regulators. The World Organisation for Animal Health addresses animal health and welfare as part of its mandate [4], and the USDA National Agricultural Library provides resources on animal health and welfare [2].

Welfare-relevant records include:

• Body condition scores over time
• Lameness incidence and treatment
• Mortality and culling rates with reasons
• Behavioral observations
• Environmental conditions (temperature, ventilation, stocking density)

Worker Safety

Record keeping also supports worker safety. Treatment records document the handling of veterinary products, which may have human health hazards. Equipment maintenance records support the safe operation of machinery. Incident records document accidents and near misses, which can guide safety improvements.

Food Safety and Traceability

Treatment records with withdrawal periods protect food safety by ensuring that meat, milk, and eggs do not contain violative residues. The U.S. Food and Drug Administration provides animal and veterinary resources that address the safe use of animal drugs [3]. Traceability records that link animals to their movements support disease response and consumer confidence.

Biosecurity

Movement records support biosecurity by documenting the sources and destinations of animals. Pig movements play a significant role in the spread of economically important infectious diseases such as African swine fever [12]. Movement records allow farmers and authorities to trace disease pathways and implement control measures.

Professional Escalation Criteria

When to Involve a Veterinarian

Records should trigger veterinary involvement when patterns suggest a health problem that exceeds the farmer's capacity to manage. Escalation criteria include:

• Disease incidence that is increasing despite treatment
• Treatment failure rates that are higher than expected
• Mortality that exceeds normal levels for the enterprise
• Reproductive performance that is below targets
• Any suspected notifiable disease

The Food and Agriculture Organization of the United Nations provides animal production and health resources that support disease prevention and response [1]. The USDA Agricultural Research Service conducts research on animal production and protection [5].

When to Involve an Adviser or Extension Specialist

Records should trigger adviser involvement when financial or production patterns suggest management problems. Escalation criteria include:

• Cost of production that is rising faster than revenue
• Feed conversion that is worsening over time
• Crop yields that are declining despite input increases
• Inability to interpret records or extract useful information

When to Involve a Regulatory Authority

Records should trigger regulatory involvement when there is a legal obligation to report. Escalation criteria include:

• Suspected notifiable disease
• Suspected food safety violation
• Suspected antimicrobial resistance pattern
• Any requirement under local or national regulations

Frequently Asked Questions

What is the simplest record keeping system for a small farm?

The simplest system is a paper ledger with pre-printed forms for the farm's main activities. Keep the forms where the work happens, such as a clipboard in the barn or a notebook in the tractor. Record the minimum data needed for decisions, and schedule a regular time each week to review entries. A small farm with one enterprise and one decision maker can function well with paper records if the forms are well designed and the analysis is consistent.

What are the best free farm record keeping software options?

Free options include general spreadsheet programs such as Google Sheets, which offer cloud storage and collaboration at no cost. Some farm management platforms offer free tiers with limited features. The best free option depends on the enterprise and the specific records needed. A spreadsheet can be adapted to almost any farm record need, while specialized free apps may be limited to specific species or functions. Test any free tool with a small data set before committing to it.

How do I create a farm record keeping template in Excel?

Start by listing the decisions the template must support. Create columns for each data field, with one row per record. Use data validation to create dropdown menus for repeated entries such as animal sex, treatment type, or field name. Use formulas to calculate totals, averages, and rates. Protect the sheet to prevent accidental changes to formulas. Test the template with real data for a month before relying on it for decisions.

What records should I keep for each animal?

The minimum records for each animal are identification, birth date, parentage, health events with treatments, reproductive events, production data, and disposal date with reason. For breeding stock, add pedigree and genetic evaluation data. For market animals, add growth rates and sale information. The specific records depend on the species and the decisions the farm makes.

How often should I analyze my farm records?

Analysis should happen at least monthly for financial records and production records. Some records need more frequent review, such as treatment records during a disease outbreak or feed records during a price change. Annual analysis is needed for enterprise profitability and breeding value estimation. The schedule should match the decision calendar of the farm.

What is the difference between a record keeping system and a decision support system?

A record keeping system captures and stores data. A decision support system uses data to generate recommendations, predictions, or rankings. For example, a record system might store milk yields, while a decision support system uses those yields to allocate feed by production group or to rank animals for culling [7]. Decision support systems require more data and more sophisticated software but can add significant value beyond simple record keeping [7].

How do I get employees to record data consistently?

Train employees on what to record, when to record it, and why it matters. Put the record where the work happens, because records located in the work area are more likely to be completed [10]. Keep the forms simple and quick to complete. Review records with employees regularly and show how the data supports decisions. Recognize employees who record consistently and address gaps promptly.

What should I do if my records show a problem I cannot explain?

If records show a pattern that is unexpected, such as rising mortality, declining yields, or increasing treatment costs, escalate to a professional. Contact a veterinarian for health patterns, an adviser or extension specialist for production or financial patterns, and a regulatory authority if there is any legal obligation to report. Bring the records to the conversation, because accurate data helps professionals diagnose problems faster.

Related Farming Guides

• Livestock Farm Performance Benchmarking Dashboard: Key Metrics and Comparison Tools
• Livestock Farm Record-Keeping System
• Camel Farm Record Keeping: Production, Health, and Financial Records
• Alpaca and Llama Farm Record Keeping: Health, Breeding, and Financial Records
• Cervid Breeding Season Management: Timing, Buck-to-Doe Ratios, and Record Keeping

References and Further Reading

• FAO Animal Production and Health. Food and Agriculture Organization of the United Nations.
• Animal Health and Welfare. USDA National Agricultural Library.
• Animal and Veterinary Resources. U.S. Food and Drug Administration.
• Animal Health and Welfare. World Organisation for Animal Health.
• Animal Production and Protection. USDA Agricultural Research Service.
• Computerized dairy reproductive herd health records.. The Veterinary clinics of North America. Food animal practice, 1987.
• FarmDain, a Decision Support System for Dairy Sheep and Goat Production.. Animals : an open access journal from MDPI, 2023.
• Development of a multi-use decision support system for scientific management and breeding of sheep.. Scientific reports, 2022.
• Progress in the use of computerised recording systems in dairy cow monitoring and extension in Malaysia.. Tropical animal health and production, 1990.
• Barriers to recording calf health data on dairy farms in Ontario.. JDS communications, 2024.
• [Translated article] Trazam project: A mobile application for tracking preparations compounded in a pharmacy department.. Farmacia hospitalaria : organo oficial de expresion cientifica de la Sociedad Espanola de Farmacia Hospitalaria, 2024.
• Social network analysis provides insights into African swine fever epidemiology.. Preventive veterinary medicine, 2016.
• Risk factors for twinning in dairy cows.. Journal of dairy science, 1998.
• Bovine mastitis epidemiology: Prevalence, risk factors, control program gaps and biosecurity recommendations to improve animal health in the Rwandan smallholder dairy farms.. 2026.
• Pigs and pasture: Drivers and characteristics of outdoor systems on the island of Ireland.. 2026.
• A multi-dimensional assessment of the 2021 mouse plague in New South Wales, Australia: Economic impacts and policy responses.. 2026.
• Guiding Principles: Reporting Elements for Gastrointestinal Organoid Research.. 2026.
• Minding the knowledge-action gap: Results from a mixed-methods study of antimicrobial use among dairy farmers in central Uganda.. 2026.
• Evaluating Effectiveness of Sustainable Livelihood Development in Rural Communities along Mara River Basin, Tanzania: What Works, What Doesn't Work, and Why?. 2026.
• Peripheral leukocyte transcriptomic changes in preweaned Holstein heifer calves with varying stages of Bovine Respiratory Disease.. 2026.
• Assessing Farm Record Keeping Behaviour among Small-Scale Poultry Farmers in the Ga East Municipality. 2010.
• Smart Buff Manager: A Co-Designed Mobile Application for Enhancing Buffalo Farm Management in Thailand. Journal of Buffalo Science, 2024.
• Woody plant wealth of Therikadu Reserve Forest, Tuticorin, India: a checklist. Journal of Threatened Taxa, 2022.
• Production function analysis of paddy farming in Sri Lanka. 1976.
• DETERMINANTS AND CONSTRAINTS OF FARM RECORD-KEEPING PRACTICE AMONG SMALL-SCALE MILLET FARMERS IN BAUCHI STATE, NIGERIA. FUDMA Journal of Sciences, 2026.
• Computer use and factors influencing computer adoption among commercial farmers in Natal Province, South Africa. Computers and Electronics in Agriculture, 1994.
• COMPUTERIZED RECORD KEEPING AND INVENTORY MANAGEMENT IN THE AGRICULTURAL EQUIPMENT INDUSTRY.. Paper American Society of Agricultural Engineers, 1985.
• A Farm Management Information System Using Future Internet Technologies. IFAC Papersonline, 2016.

This article is educational and is not a substitute for veterinary diagnosis, treatment, public-health guidance, or regulatory reporting.