MICROCOMPUTER BUDGET MANAGEMENT SYSTEM

The enterprise budget, whole farm cash flow, and income statement are fundamental tools of farm and ranch management. The "Microcomputer Budget Management System" (MBMS) is a microcomputer software package that facilitates the storage and use of information for crop and livestock budgeting. It performs the calculations for several enterprise budgeting formats and for preparation of whole farm resource use reports and financial statements. The MBMS also includes internal machinery and irrigation cost calculation routines. MBMS was developed for use by extension staff, researchers, lenders, consultants, and operators of diversified farms and ranches with many enterprises that use enterprise and whole farm budgeting for analysis and planning activities. The flexibility and detailed nature of the program requires the user to have knowledge of enterprise budgeting and operation of complex computer programs. This paper presents a discussion of the features and capabilities of the software and the computational procedures used in the cost calculations.Research and Development/Tech Change/Emerging Technologies,

Pattern, Trend and Determinants of Crop Diversification: Empirical Evidence from Smallholders in Eastern Ethiopia

Crop diversification is the most important risk management strategies. The study investigated the pattern, trend and covariates of crop diversification in eastern Ethiopia based on data collected from 167 households randomly and proportionately selected. In order to manage risks of drought, pests and diseases, soil fertility decline and input prices variations, farmers in the study areas employ crop diversification as a self-insuring strategy. The farmers are becoming risk-averse which has implications on technology adoption. Tobit model result indicated that farmers with more extension contacts and larger livestock size are likely to specialize whereas those who have access to market information and irrigation, those who own machinery and more number of farm plots are more likely to diversify. In order to promote crop diversification, providing farm machinery through easy loans and improving access to market information and irrigation should be given attention. The extension system should include risk-minimization as a strategy. Keywords: crop diversification, risk, risk management strategies, risk-averse, Ethiopia

Is the Farm Prospering and Why? A Method to Spot Good or Weak Performance and To Do "Benchmarking"

In Swedish agriculture "mixed farming" is common. The manager may choose among several crops, with or without livestock production, or to produce services to other farmers or to customers outside the farm sector. Farmers with diversified production feel the need to know which enterprises that do really contribute to farm profitability on the whole even if gross margins appears to be rather good. With our method we evaluate the economic result of the most recent year in cooperation with the farmer. We analyse the profit and loss account based on farm records on quantities and monetary information. Revenues and costs are allocated to the enterprises where they belong. The systematic approach includes even farm enterprises that produce "internal products" i.e. feed production, straw production etc. This procedure is implemented, not only for the easily found variable costs, but also for labour, machinery and energy costs as well as for for buildings and some other capacity costs. We use definitions of gross margin 1, gross margin 2, etc in steps down towards finally computing the rate of the return on investments. The system produces results both on the farm business- and enterprise level on many key numbers regarding economic and technical efficiency. In the end we undertake "benchmarking actvities" individually (with the farm-staff) and with groups of farmers through organized workshops. This method has been in use since 1949 with 120 customers currently utilising the system.Farm Management,

Farm Management Information System for High Productivity in Agribusiness

The focus of this study is to present Farm Management Information System (FMIS) and its application in Agribusiness. FMIS is a software designed for high productivity in farming business (fish farming as a case study) and to assist agricultural farmers to perform various tasks with ease such as operational planning, implementation, documentation, and application for financial subsidies or grants. The study presents the template that could be used for fish farmers in order to ease their tasks. FMIS could be used by different stakeholders such as farmers, government organizations, service providers, and machinery or equipment or tools manufacturers to transfer information among each other. This paper discovered that lack of interoperability, insufficient stakeholder’s collaboration and a not clearly defined business model has hampered the proper functioning and adaptation of useful Information Communication Technologies (ICT’s) such as the FMIS. Manual approach is limited in the affairs of better farm fish management, one method by which this can be improved is by support system which this work focuses to address. The FMIS software designed uses C#, Visual Studio and SQL Server, will assist the users or Fish or Farm managers in solving their day to day problems such as accurate stocking record, sales/harvesting record, payment record among others. The study concluded that with the application of FMIS in Fish farming, the processing of farm information activities can be automated to a large extent, thereby reducing processing time an

Voice-driven fleet management system for agricultural operations

Food consumption is constantly increasing at global scale. In this light, agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products. However, due to by environmental and biological factors (e.g. soil compaction) the weight and size of the machinery cannot be further physically optimized. Thus, only marginal improvements are possible to increase equipment effectiveness. On the contrary, late technological advances in ICT provide the ground for significant improvements in agri-production efficiency. In this work, the V-Agrifleet tool is presented and demonstrated. V-Agrifleet is developed to provide a “hands-free” interface for information exchange and an “Olympic view” to all coordinated users, giving them the ability for decentralized decision-making. The proposed tool can be used by the end-users (e.g. farmers, contractors, farm associations, agri-products storage and processing facilities, etc.) order to optimize task and time management. The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations. Its vendor-independent architecture, voice-driven interaction, context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system

Management of investment processes on Finnish farms

Structural change in agriculture means a continuous need for investing in farm production. It is essential for the sustainable operations and the economy of the farm that such investments are successful. In this research, different stages of the investment process of farms were studied as well as the use of information and the success perceived during the investment process. The study was carried out with mail surveys and telephone interviews on the Finnish Farm Accountancy Data Network (FADN) farms. The most challenging investments were in animal husbandry buildings and, as to these investments, the comparison of alternatives was the most challenging stage. For most investments, the planning phase was considered more challenging than the implementation. Before making the decision, farmers acquired information from many sources, of which the opinion of the main customer and the experiences of fellow farmers were the most valued. Some of the products considered were so new on the market that it was not easy to get adequate information and, furthermore, the information given by suppliers was not always accurate. Decision-making was supported by calculations, but qualitative factors had a dominating role. Large basic decisions were made relatively quickly, while details needed a longer time to process. In general, farm managers were satisfied with their investments. Improvements in work quality and quantity were especially mentioned and generally qualitative factors were the ones first in mind when evaluating the successfulness of the investment

Organic Farming Scenarios: Operational Analysis and Costs of implementing Innovative Technologies

The objective of this study has been to design a number of farm scenarios representing future plausible and internally consistent organic farming enterprises based on milk, pig, and plant production and use these farm scenarios as the basis for the generation of generalised knowledge on labour and machinery input and costs. Also, an impact analysis and feasibility study of introducing innovative technologies into the organic production system has been invoked. The labour demand for the production farms ranged from 61 to 253hha1 and from 194 to 396hLU1 (LU is livestock units) for work in the animal houses. Model validation results showed that farm managerial tasks amount to 14–19% of the total labour requirement. The impact of introducing new technologies and work methods related to organic farming was evaluated using two innovative examples of weed control: a weeding robot and an integrated system for band steaming. While these technologies increased the capital investment required, the labour demand was reduced by 83–85% in sugar beet and 60% in carrots, which would improve profitability by 72–85% if fully utilised. Profitability is reduced, if automation efforts result in insufficient weed removal compared to manual weeding. Specifically, the benefit gained by robotic weeding was sensitive to the weed intensity and the initial price of the equipment, but a weeding efficiency of under 25% is required to make it unprofitable. This approach demonstrates the feasibility of applying and testing operational models in organic farming systems in the continued evaluation and documentation of labour and machinery inputs

Organic farm incomes in England and Wales 1999/00 and 2000/01

This report presents results from research work carried out for the Department for Environment, Food and Rural Affairs (DEFRA) on the financial performance of organic farms for 1999/00 and 2000/01 financial years. The aim of the report is to collate financial data for organic farms differentiated by farm type, in order to provide continuity between previous studies (project code: OF 0190) on the economics of organic farming covering years 1995/96 to 1998/99 and new research work carried out by the Institute of Rural Sciences, UWA, to obtain financial information for organic farms for the period 2001/02 to 2003/04 (project code: OF 0189). The financial data in this report were not collected directly, but were derived from other DEFRA-funded studies. Data are from farms of varied sizes within the samples for each farm type and not always of adequate sample size. This was particularly the case for horticulture while other datasets mainly comprised five farms or more per farm type. A cautious approach is required when viewing smaller farm samples as it is not possible to draw conclusions on the organic sector from these results; but may permit observation of data trends for the particular set of farms within the sample. Where possible, to provide an idea of economic trends over time, continuous farm data for 1998/99 and in some cases for 2001/02 are shown alongside the 1999/00 and 2000/01 data. The report highlights results for organic cropping, horticulture, dairy, lowland and LFA cattle and sheep farms and one set of results for in-conversion dairy farms. Comparable conventional farm datasets are shown alongside some organic datasets for comparison. This was the case for both organic and in-conversion dairy farms and LFA and lowland cattle and sheep farms for 2000/01 datasets only. From this report, the financial data show that most farm types under organic management had positive net farm incomes (NFI) with the exception of the in-conversion dairy farm sample. Management and investment incomes (MII) were positive values for all farm types with the exception of lowland and LFA cattle and sheep farms from the FBS sample. The financial trends varied by farm type between 1999/00 and 2000/01 with the organic cropping farm sample experiencing over 60% reduction in NFI, whilst organic dairy and LFA cattle and sheep farm incomes increased at varying levels over the two years. For all farm types where comparable data are shown alongside the organic farm sample, the organic sample showed higher NFI and MII values with the exception of the in-conversion dairy farm sample where income values were lower than the comparable dataset. Gross margin data are presented for organic dairy herds, LFA suckler cows and finished beef stock and LFA breeding ewe flocks. Arable gross margins are shown for winter wheat, spring wheat, oats, beans and potatoes crops and horticultural data are available for potatoes, carrots, beetroot and calabrese

Organic farm incomes in England and Wales 2001/02

Financial results from research work carried out for the Department of Environment, Food and Rural Affairs (DEFRA) by the Organic Farming Research Unit at the IRS, UWA on the economic performance of organic farms in 2001/02 are presented in this report. A fundamental aim of this work is to assess the financial performance of organic farms differentiated by farm type, in order to inform DEFRA policy-making with respect to economics of organic farming, and to provide a basis for assessments by farmers, advisers and other interested parties of the farm-level implications of conversion to and continued organic farming. This research area builds on previous economics work on organic farming carried out by IRS, UWA (Project OF0190, covering 1995/961 to 1998/992). Here, data is shown for the 2001/02 financial year, which is the first of a series of three reports covering the financial performance of organic farm types including cropping, horticulture, lowland and LFA dairy, lowland and LFA cattle and sheep and mixed farming systems for 2001/02 up to 2003/04. In comparison with the earlier reports, there has been a significant improvement in the numbers of farms for which data have been obtained. Summarised and detailed financial input, output, income, liabilities and assets and some physical performance measures are presented based on current Farm Business Survey data collection and collation guidelines. The samples of organic farms per robust farm type are sufficiently large to give a reasonable level of confidence in the data; however, it should be noted that the organic farm samples are not statistically representative of their type, although the results can be seen as a reasonable indication of farm income levels for organic farms. An additional element of this work is the inclusion of comparable conventional farm data for the farm types shown. Each organic farm within this study was matched with the averaged results for a comparable cluster of conventional farms based on the resource endowment of individual organic farms. Broadly speaking, the parameters used to select comparable farm clusters included farm type, FBS region, LFA status, utilisable agricultural area, milk quota holding (where applicable) and farm business size. For each farm type, the results for each cluster were averaged and compared with the average for the individual organic farms. Overall, organic farms showed a similar or higher level of net farm income for all farm types compared to the conventional farms. The greatest differences were seen in the cropping, horticulture, LFA dairy and mixed farm types. Both organic and conventional lowland dairy types performed similarly. For management and investment income, only the organic lowland and LFA cattle and sheep farms showed a negative value. Conversely, the comparable conventional farm types showed a negative value with the exception of the lowland dairy farms. Gross margin data is presented for organic dairy herds including the top and bottom 5 performing herds. Cattle and sheep gross margins are shown for lowland and LFA farm types in addition to breeding pig gross margins. Crops shown include winter and spring wheat and barley, spring oats, beans and potatoes and a further five horticultural crops