Republic of Macedonia Farm Business Data 2001/2002

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1 FARM BUSINESS DATA MACEDONIA Provisional data Skopje, July, 2002 Ben Kamphuis Lazo Dimitrov CONTENTS Preface Introduction The Financial Farm Monitoring Program Project Approach The results This report Standard Gross Margins and Farm Typology Calculation of Standard Gross Margin Farm typology Farm Structure by Farm type Farm size Land use Livestock Financial Results All farms By farm type By farm size By region National Extension Service of Macedonia 1

2 PREFACE Policy making without adequate information is impossible and it is therefore that the Ministry of Agriculture, Forestry and Water Economy (MAFWE) took the initiative to establish a financial farm monitoring system under the umbrella of the. In the frame of that project a methodology following the EU requirements but suited to the Macedonian circumstances has been developed and applied for the season 2001/2002. This report contains the first results of the analysis of the data provided by hundreds of farmers all over Macedonia. I would like to thank them for their co-operation and I sincerely hope that they will continue in providing this type of data in the future. In due time they will benefit from the results, directly by getting a better understanding of their own business and indirectly via more appropriate policy measures. The National Extension Agency has carried out data collection and processing. Without the input of its staff it should not have been possible to include so many farmers in the network. I am grateful for that and would like to thank them all but in particular Ms. Vesna Ilievska and Mr. Mitko Kostov who were responsible for the implementation of the system at local and central level, including software development and installation, training of staff and data processing. Last but not least I would like to name here Mr. Lazo Dimitrov, the local expert engaged for this project by the PFSP project office. I very much enjoyed working with him, discussing the various alternatives in all stages of the process, from the development of the farmers' notebook to the contents of this report. He was the person that put the ideas into reality by designing the required notebooks, communicating with NEA management and training NEA staff at central and local level. Without his enthusiasm, perseverance and creativity the project should not have been so well developed as it has. The importance of this project reaches much further than this report with the first financial results of private farmers in Macedonia in The project succeeded in laying the foundation for a Farm Accountancy Data Network (FADN) that is required by the European Union for all member states and consequently for all candidate countries. For that reason it is important that Macedonia succeeds in expanding and strengthening the system. The weakest link at the moment is the data analysing capacity. That is one of the reasons that this report reveals only a glimpse of the total information that is stored in the FMS database of NEA. Further technical support by EU experts is, therefore, highly recommended as well as financial support to the institutions that are responsible for the implementation of the monitoring system. Mr. Ben Kamphuis Agricultural Information Expert ( PFSP), Macedonia Agricultural Economics Research Institute (LEI), The Netherlands National Extension Service of Macedonia 2

3 National Extension Service of Macedonia 3

4 1 INTRODUCTION 1.1 The Financial Farm Monitoring Program The agricultural sector in Macedonia is in a process of restructuring. Adequate information is indispensable in this process, but is almost not available. To address part of this problem the Ministry of Agriculture, Forestry and Water Economy (MAFWE), supported by World Bank-Private Farm Support Project (PFSP), initiated the "Financial Farm Monitoring Program" (FFMP). This program was aimed at establishing a system for regularly monitoring the private farmers sector in Macedonia in order to get more reliable data on the financial situation of the private farmers in Macedonia at national and regional level. The Farm Monitoring System (FMS) was prepared by a Macedonian and an EU-expert while the system was implemented by the National Extension Agency of the Republic of Macedonia (NEA). The project was carried out in seven phases. A detailed description of the activities can be found in the project reports. Phase Activities Month Year 1. Selection and recruitment of farmers April Preparation of questionnaires and notebooks March/April Preparation of data collection March Preparation of data entering and processing April/May Implementation of data collection and data entering February - February 2001/02 6. Data analysis and reporting January - July Evaluation July Project Approach a. Selection of FMS farms The project was aimed at setting up a farm monitoring system that should be representative for the individual farmers in Macedonia. It was, however, clear from the start of the project that a fully representative system could not be realised because of the statistical data needed for that purpose were not available in Macedonia. Starting point for the monitoring system were the about 1000 farmers in the network of NEA. From this group 450 farms have been selected on the basis of the data of a pilot farm survey carried out by the State Statistical Office (SSO) of Macedonia. The most important criteria used for the selection were farm type and farm size. Because a farm typology was not yet developed for Macedonia the WB-project team constructed a provisional farm typology that followed the EU-approach and was based on Standard Gross Margins (SGM) per crop and animal. The used SGM were based on figures in available reports, calculations and estimations of NEA advisors and the WB-project team. Despite these efforts the FMS-sample should be considered as an approximation, because (a) the SSO pilot survey was not representative, (b) the typology was based on rather tentative SGM's and (c) the selection was restricted to farmers who had already contact with NEA. It was, however, the best possible solution for that moment. The upcoming Agricultural Census, which is due to be held in 2003 will considerably improve the possibilities for constructing a representative FMS-sample. b. Data collecting and processing system The project was aimed at laying the foundation for a monitoring system that meets the requirements of the European Union with respect to the so-called Farm Accountancy Data Network (FADN). Most farmers in Macedonia, however, are not used to regularly keeping records on technical and financial aspects of the farm. For that reason it was decided to develop a simple monitoring system in which the farmers themselves only have to note the expenses and revenues and the NEA-advisors collect the other relevant data in consultation with the farmers. The FMS follows largely the FADN methodology but not in all details. It is expected that in course of time the system can easily be upgraded into a complete FADN. It is also for that reason that NEA developed a professional database in MS-Access database, including the required entering screens and users' applications. National Extension Service of Macedonia 4

5 d. Analysis and Reporting It was planned to prepare at the end of the bookkeeping year different reports: standard business reports for each farmer that can be used for farm management (advise), and standard and specific reports on farm business performance at regional and national level for NEA and other institutions involved in agriculture in Macedonia. However, because of time constraints only this report with the major results on the bookkeeping year 2001/2002 has been drawn up. NEA has the intention to make individual farmreports based on the calculations prepared by the project team and it is expected that the data will also be used for further analysis by NEA and other institutions in Macedonia. 1.3 The results Number of farms in the system The intention was to start data collection in February at the start of the growing season in Macedonia. The data collection system, notebooks etc, however, were not ready in time because of the late start of the project. Fortunately, NEA has already started collecting data so that most of the data could be collected according to the planning. NEA did not only collect data of the selected 450 farms but also of the rest of farms in its network, about 600 farms, following its own data collection system. About 800 farms continued to deliver the requested data during the whole bookkeeping year (February February 2002). A number of the selected 450 FMS-farms have been replaced by others and the data of these farms formed the basis for the first analysis, which took place in June It was the intention to use the data of the FMS-farms in accordance to their weight in the sample, but this has not been carried out, eventually, partly because of the uncertainties on the quality of the data and partly because of time constraints in the final project phase. This means that all farms are used equally in the calculations, so that it might be possible that in a certain farm type the smaller or larger farms are over represented or just the other way around and vice versa that in some size classes some farm types are over- or under represented. For practical reasons NEA fixed the number of FMS-farms per region on 75. It has not been checked whether these farms less or more represent the farms in the region as for farm type and size, so it might be possible that the data per region are not in line with the reality in the regions. Quality of the data It was planned that the advisors of the local units should collect the data, while the data should be checked and entered into the database at regional level. The central staff of NEA should carry out further data checking. In that way it should have been possible to divide the responsibilities and workload over the different levels in the organisation and to provide level by level training. NEA, however, decided not to make use of the intermediate role of the regional offices. That decision lead to a considerable increase of the workload of the involved central staff, because they needed to visit the 30 local units in stead of the 6 regional offices for the installation of the database system, training of local staff and data checking. Consequently, also the local FMS-expert needed to provide assistance at local level instead of regional level. As a result, the support was took much time but was less intensive than was foreseen what might have had an influence on the quality of the data. It was the intention to develop an electronic quality checking system on the consistency and plausibility of the data. Such a system was not developed due to time constraints at central level. The collected data have been checked only visually at local level by the advisors and also at central level after data entering. The EU-expert did not have access to the individual data but he received aggregated data. The first analysis by the EU-expert showed that some of the calculated figures were not plausible and for that reason an extra check have been carried out by NEA resulting in 43 farms to be excluded from the calculations, leaving 417 farms for reporting. There are still some inconsistencies in the tables, but the very short period for analysis does not allow further checks. It is, therefore, strongly recommended to further analyse and check the data on completeness, consistency and plausibility, before further reporting. National Extension Service of Macedonia 5

6 1.4 This report This report contains the major results of the FMS network. The following chapter contains the calculation of Standard Gross Margins for the major crops and animals and the farm typology. It can be considered as the first overall farm-typology of Macedonia, although the above-mentioned doubts on the representativeness of the FMS-farms. This farm typology is taken as the starting point for the description of the farm structure (Chapter 3) and the financial results of the farms (Chapter 4). It was planned to include more technical and financial data on the different farm types in the report, but the few project days available for analysis and reporting was not sufficient for that. It might be clear that the figures in this report need to be used with care, firstly because of the uncertainties with respect of the completeness of the used data, secondly because the figures are based on farm data of just one year and thirdly because there are still inconsistencies between the figures in this report. In spite of that are the data are much better and complete than ever before published in Macedonia and give for the first time the possibility to better classify the farms following the EU-standards and to provide information on farm income. It is, however, highly recommended to contact NEA before using the data and request them to provide revised tables. National Extension Service of Macedonia 6

7 2 STANDARD GROSS MARGINS AND FARM TYPOLOGY 2.1 Calculation of Standard Gross Margin For the selection of the monitoring-farms at the start of the project a provisional Farm-typology for Macedonia has been constructed on the basis of mainly estimated Standard Gross Margins per (group of) crops and animals. Based on the collected data, however, it is possible to provide a far more reliable set of SGM's. Because of the aforementioned incompleteness of the data the project team constructed a specific algorithm for these calculations. A short description follows here: Because the physical data on the yields are more complete and more reliable than the financial data, the total returns per crop and animal have been calculated by multiplying the yield per ha and animal in physical terms with the average price received for the sold products per farm. The direct costs of animal production have been calculated on the basis of the available financial data, but that was not possible for all crops. In general, the direct costs per crop are based on the figures of those farms that had specified these items per crop. In case for a certain crop only a few specified figures were not available the average figure of the whole group was put in place. In case most items were not specified the respective crop was add to the group of "other crops" with average figures for that group. This approach resulted in Standard Gross Margins for milking cows, sheep and 15 crops, as shown in table 2.1. The differences between the different crops raise some questions, but it was not possible to analyse the primary data. Table 2.1 Gross margin calculation for some major crops/animals (in Denars) Crop/animal Calculated Gross Calculated Direct Output Costs Gross Margin Wheat 29,972 9,941 20,031 Barley 31,238 6,137 25,101 Corn 84,522 10,262 74,260 Tomato 968,567 70, ,044 Pepper 480,040 26, ,559 Watermelon 259,734 7, ,303 Potato 284,065 61, ,773 Onion 291,024 12, ,081 Cabbage 188,426 17, ,534 Beans 141,075 8, ,403 Plums 160,000 3, ,511 Apple 373,442 79, ,061 Grape 168,557 18, ,491 Tobacco 203,565 4, ,376 Lucerne 108,937 6, ,080 Milking cow 88,669 37,497 51,172 Sheep 1,897 1, Farm typology Based on the calculated SGM per unit of crops and livestock the different activities of a farm can be valued in economic terms. Through aggregation of these values the Gross Margin for every farm can be calculated, which is an indicator for the economic size of a farm. The Gross Margin of a farm is equal to the Gross Revenues minus the Direct Costs. The relative contribution (in %) of the different activities to the total SGM of the farm forms the basis for the classification in different types of farming. The farm types used in the FMS project are described in table 2.2. This table gives also the division of the 417 FMS-farms used in this report over the farm types compared with the farms of a pilot survey of the Statistical Office of Macedonia in 2000 (SSO-pilot). The major difference is in the mixed animal and mixed other farms. That might be the result of differences in the SGM used. It needs further to be noted here, that also the SSO sample was probably not representative, because it was based on outdated statistics. The agricultural census that is due to being carried out in 2003 will provide the first possible basis for a reliable sample. National Extension Service of Macedonia 7

8 Table 2.2 Farm Typology Macedonia Number Percentage of farms Farm type Definition of farm type of FMSnetworpilot SSO- farms A. Vegetable growers More than 2/3 of the production from vegetable growing B. Fruit producers More than 2/3 of the production from fruit/orchards C. Vini-culturists More than 2/3 of the production from vineyards D. Arable farms More than 2/3 of the production from arable crops E. Mixed plant farms More than 2/3 of the production from plant production F. Cattle farms More than 2/3 of the production from cows and other cattle G. Sheep farms More than 2/3 of the production from sheep and goats H. Mixed animal farms More than 2/3 of the production from animal production I. Mixed farms Not any activity more than 2/3 of the total production farms The figures in table 2.2 confirm that most of the farms in Macedonia combine different farm activities. Almost 60% of the FMS-farms are not specialised in one of the farm activities. Table 2.3 shows further that the specialised farms in the FMS network are mostly very specialised, having on an average more than 85% of the production in the main farm activity. Table 2.3 Farm activities by farm type Farm Share of the following farm activities in the total farm gross margin (in percentages) Farm type Gross Margin Arable Other Vegetables Orchards Vineyards Cattle Sheep (1000 Denar) land animals A. Vegetable growers B. Fruit producers C. Vini-culturists D. Arable farms E. Mixed plant farms F. Cattle farms G. Sheep farms H. Mixed animal farms I. Mixed farms farms 3, National Extension Service of Macedonia 8

9 3 FARM STRUCTURE BY FARM TYPE 3.1 Farm size The FMS-farms differ considerably in farm size, as it is shown in table 3.1. The average ha of cultivated land of all farms is 3.5 ha, varying from an average of about one ha of the sheep farms till more that seven ha for the arable farms. Less than a quarter of the farms have less than 1 hectare, while only six percent has more than 10 hectare. Most sheep farms in the sample have less than one hectare of cultivated land. They graze their sheep on the pastures in the mountains. More than 80 % of the fruit and grape producers have less than 2 hectares. Most of the vegetables growers have less than 5 hectare of land. The largest group in the sample, the mixed plant farms and the mixed farms show a more standard distribution of the farms over the farm size categories. Table 3.1. Size of the farms by farm type (cultivated land) Of which farms with... ha of cultivated land Average ha number of less than more than Farm type per farm farms 1.0 ha ha ha ha 10 ha A. Vegetable growers % 2.39 B. Fruit producers % 2.10 C. Vini-culturists % 1.31 D. Arable farms % 7.28 E. Mixed plant farms % 4.40 F. Cattle farms % 2.93 G. Sheep farms % 1.04 H. Mixed animal farms % 2.60 I. Mixed farms % 4.05 farms % Land use The average hectare per farm figures in table 3.2 do not correspond with the figures in table 3.1, but in spite of that some observations can be made. Table 3.2 shows that it is common in Macedonia to combine different types of land use and that most farms have arable land. The table gives also evidence of the fact that cattle farming in Macedonia is not based on grazing in meadows but on fodder crops. As it has been said above, most of the sheep farms use pastures for grazing, on an average about 3 hectare. Table 3.2. Land use by farm type Farm type Land use in hectares per farm (average figures per farm type) number of Arable Vegetables Orchards Vineyards farms land Meadows cultivated Pastures agricultural land land A. Vegetable growers B. Fruit producers C. Vini-culturists D. Arable farms E. Mixed plant farms F. Cattle farms G. Sheep farms H. Mixed animal farms I. Mixed farms farms National Extension Service of Macedonia 9

10 3.3 Livestock As it is mentioned before many farms in Macedonia combine plant and animal production. Table 3.3 shows that also the combination of different types of animals is rather common, except for the specialised farms with fruit and vineyards. The specialised cattle farms in the sample have nine milking cows on an average. The sheep farms in the sample are rather large with an average of 450 sheep per farm. Table 3.3. Livestock by farm type Number of animals per farm (average figures per farm type) Farm type number of Milking Other Sheep Goats Pigs farms cows Cattle Poultry A. Vegetable growers B. Fruit producers C. Vini-culturists D. Arable farms E. Mixed plant farms F. Cattle farms G. Sheep farms H. Mixed animal farms I. Mixed farms farms National Extension Service of Macedonia 10

11 4 FINANCIAL RESULTS 4.1 All farms The major purpose of the project was to provide a better view on the income situation on the farms. The farmers have been asked to note all their incomes and payments and also the technical results such as the total production of grains and the milk yield per cow. By combining these data the total revenues of the farms have been calculated as well as the direct costs, following the approach described in chapter 2. This means that the figures in this chapter are the best possible approximations of the real financial situation of the farms. The figures in table 4.1 and figure 4.1 show that there are considerable differences in Farm Gross Margin between the farms but that most of them have a low income; 70% of the farms have a gross margin under 500,000 Macedonian Denar. Figure 4.1Differences in gross margin per farm Percentage of farms SGM per farm (1000 denar) Table 4.1 Differences in gross margin per farm FMS farms Upper limits of categories of SGM per farm in 1000 Denar < 100 < 200 < 300 < 400 < 500 < 600 < 700 < 800 < 900 < 1000 < 1500 >1500 total Number Percentage In the following sections the financial results will be presented by farm type, farm size and region. 4.2 By farm type The financial figures in table 4.2 show large differences between the farm types. The total farm returns of the animal farms, in particular the cattle farms are much bigger than those of the other farm types are. The calculated direct costs are also higher but still leaving a higher gross margin for the animal farms than the other ones. The gross margin per cattle and mixed animal farm is around 600,000 Denar, for the sheep and mixed farms above 350,000, while the average of the other farm types is less than 250,000 Denar. The viticulturist had even a negative gross margin, which is rather strange because the SGM for grapes is positive. Further analysis of the farm data could reveal the causes for these differences. The gross margin does not reflect the total costs of the farm; fixed costs for land (rent), capital (interest) and labour (wages) are not included. Parts of these cost are expenditures for the farmer, part of it not. In this report only the recorded "paid fixed costs" are taken into account and subtracted from the farm gross National Extension Service of Macedonia 11

12 margin. The remainder is the money that can/have been spent by the farmer for farm and family. The differences in "income for farm and family" per farm type are about the same as for the gross margin. Table 4.2 Gross margin by farm type (average figures per farm in 1000 Denar) Farm type number of farms calculated farm returns calculated direct costs Calculated farm gross margin paid general costs Income for Gross margin farm and per ha family cultivated land A. Vegetable growers B. Fruit producers C. Vini-culturists D. Arable farms E. Mixed plant farms F. Cattle farms 29 1, G. Sheep farms 13 1,832 1, H. Mixed animal farms 28 1, I. Mixed farms farms By farm size This section gives some figures on the gross margin and the income situation by farm size. In general the gross margin per hectare is relative high for the smaller sized farms, because they are using the land for intensive production, like the vegetables and fruit growers. The average gross margin of the farms with less than 2 hectare is two times the overall average of the FMS farms. The larger farms, however, realise a bigger gross margin and income for farm and family than the smaller farms. The average calculated income for the farms with more than 10 hectare of land is about Denar and for the farms with less than 2 hectare , while the latter group represents half of the farmers in Macedonia (see section 3.1). Table 4.3 Gross Margin by farm size (Average figures per farm in 1000 Denar) Farm size calculated farm returns calculated direct costs Calculated farm gross margin paid general costs Income for farm and family Ha cultivated land per farm Gross margin per ha cultivated land less than 2 ha ha ha 1, ha 1, more than 15 ha 1, Average farm By region As it is mentioned in the Introduction the FMS-farms have not been selected proportional to the number of farms by farm type and farm size in the regions. Consequently it is questionable whether they represent the real situation in the regions and the following figures should, therefore, be considered as indications only. According to these data the financial situation is worse in Strumica while the farmers in Tetovo have the highest income on the average, partly caused by large differences in the total paid general costs. Further analysis is needed to clarify the differences. Table 4.4 Gross margin by region (average figures per farm in 1000 Denar) Region calculated farm returns calculated direct costs Calculated farm gross margin paid general costs Income for farm and family Ha cultivated land per farm Average gross margin per ha Tetovo 1, Skopje Bitola Strumica Kumanova Stip Average farm National Extension Service of Macedonia 12

13 National Extension Service of Macedonia 13

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