10.5 Economic viability

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10.5. 1 Overview

Tools for economic analysis are probably the least known on FSD teams, which is why some time is spent in this section outlining some of the main tools. Many other references also deal with the topics in much more detail.

Most limited-resource farming households produce a mix of products for household use and sometimes for selling, Livestock (i.e., cattle or small stock) may be sold whereas basic grain production is, if not subsistence production, is at least substitution production. There are many methods for analysing potential improved technologies in a formal manner from an economic viewpoint. Three critical issues underlying the legitimacy of many of the results of such analysis are:

• Measurement Problems in Doing On-Farm Trials in FSD. At times, it is not possible to collect all of the data needed for an economic analysis, or the data collected are suspect. It may be necessary to make estimations of individual data values or to use coefficients for some activities (i.e., particularly labour coefficients) in order to perform a particular analysis. With integrated trial design involving all team members and close supervision of field activities, the need for making estimates and for using coefficients can be reduced. Although it may be practical to use standardized coefficients for some labour activities, for example, broadcast planting or ploughing, which are common to all technologies being tested, it is important to collect actual labour data for activities that differ between treatments, such as weeding times where there is an additional tillage operation that may reduce weed burden on one of the trial plots. Obviously, it is desirable to have all measurements as accurate as possible, but given field conditions, it may be necessary to settle for less than perfect data. Thus, the analysis performed should be appropriate for the quality of data that are collected.
• Placing Correct Values on the Inputs and Products not Originating from or Ending up in the Market Place. On the input side, for example, a particularly challenging issue is that of valuing family labour of which, the demand, and, therefore, the value are likely to vary seasonally. On the output side, there are many issues, for example, problems relating to valuing production consumed by household members, valuing manure from livestock that is used in production of crops, etc. However, guidelines are available on how to deal with these difficult issues. No matter how inputs and outputs are valued, it is important to explain carefully what has been done when reporting the results, so that others can evaluate the criteria for relevance to their situation and make adjustments accordingly.
• The Use of Evaluation Criteria Reflecting Those Important to the Household. Some issues relating to these have been covered in an earlier discussion (Section 10,3), One important requirement is to evaluate the results in terms of the most limiting factor, for example, return per hour used during critical bottleneck periods, rather than return per hectare. The latter is usually the major evaluation criterion used on experiment stations. Inappropriate use of such a criterion could leave researchers advocating irrelevant technologies and screening out others that could have been relevant.

As indicated earlier, techniques for economic analysis in FSD continue to be relatively simple. This is so because the relatively simple techniques are most readily understood by planners and non-economists, and because field data often are not sufficiently accurate to support complex analytical techniques. This latter reason is particularly significant where the price for most of the inputs and commodities is not determined in a market. Generally, economic analysis is carried out on the 'old' as well as the 'new, technology, in order to compare the technologies and to identify changes in the whole farm system caused by changing one part of the system.

The majority of economic analyses with reference to on-farm trial work in FSD fit in three categories:

• Average Returns Analysis. This analysis is the basis for most of the other analyses and consists of a listing of the average costs of producing a particular product and the average value of the product for each technology being compared. The cost- and-returns analysis requires information on both variable and fixed inputs, A more limited average variable cost-and-returns analysis often is used to compare different technologies that used the same fixed inputs. This information can be used to compare the average returns above variable costs (RAVC) (i.e., sometimes called gross margin analysis) for different technologies and the returns to other production factors such as total labour or ploughing labour, The process of valuing inputs and commodities its of particular importance in making realistic cost-and-return analyses.
• Budget Analysis. There are several types of budget analysis. The enterprise budget is a statement of costs-and-returns (i.e., both variable and fixed) for a particular enterprise or technology. This type of budget can be used as a building block in making whole farm budgets and in estimating the impact of a change to the new technology. The partial budget, on the other hand, is a direct comparison of the elements within enterprise budgets that change between technologies. This type of budget requires fewer data than the enterprise budget and offers the advantage of direct comparison. Finally, whole farm budgets can be used to look at allocation of resources between enterprises and at the impact of a new technology on the allocation of resources to other enterprises on a farm. The partial budget technique is used most frequently in FSD.
• Risk Analysis. When a farmer undertakes a crop or livestock enterprise, she/he aIways faces the risk of failure and loss of their time, cash, or other inputs invested in the enterprise. When farmers consider a new technology, they are concerned about the risk involved compared to the risk of their present technology. Measuring risk is difficult and of somewhat limited value, because different farmers look at risk differently. Risk analysis needs to be kept as simple as possible. Some indications of risk can be obtained from doing sensitivity analysis with partial budgets. Two additional tools used are stochastic dominance analysis, which assumes that farmers prefer more profit to less, and a modified safety-fret analysis, which provides information on the likelihood of returns from a technology falling below a minimum acceptable level.

Another type of economic analysis that is highly desirable is marginal analysis. Average returns and budget analysis are based on average data values acquired from a number of replications of a trial, all of which use about the same level of variable inputs. Thus, comparisons are being made between technologies based on a given level of inputs. Marginal analysis goes beyond the comparison of a given level of inputs and looks at profitability as levels of variable inputs change, This addition allows determinations as to the best (i.e., most efficient and profitable) allocation of resources for a given enterprise, Unfortunately, marginal analysis requires data over a wide range of inputs, something that is not available in much FSD work, so marginal analysis techniques, although valuable, have not been used widely, In practice, types of data required for marginal analysis are likely to be available only from RMRI type trials, which are not a major preoccupation of FSD work.

Therefore, the type of data available will influence the type of economic analysis that is possible. In the following sections, three major types of economic analyses commonly used are described briefly, namely average returns above variable costs (i.e., gross margin) analysis, partial budgeting and sensitivity analysis, and simplified risk analysis,

10.5.2 Gross Margin Analysis

An analysis of the average (i.e., mean) costs/benefits from different technologies being examined in a trial often is made, Where all of the relevant data have been collected, this type of analysis can be used, However, this requires valuation of both fixed and variable inputs, In many cases, the variable inputs are what need to be examined, because the fixed inputs are constant for all plots in the trial, Thus, the most common analysis performed involves the average returns above variable costs (RAVC) or gross margin approach. This allows a comparison between various technologies being tested based on the inputs the farmer must provide.

The procedure for gross margin analysis is:

• Calculate an average yield or an average amount of product for each separate technology,
• Calculate average inputs -- often with particular emphasis on labour inputs -- for each technology separately.
• Calculate the gross return (i.e., gross total value product), which is the yield times the appropriate field price for the product or products,
• Calculate the variable costs associated with each technology. The variable costs for a crop trial usually include labour; seed; draught hire (i.e., if hired draught is used); and a charge for equipment depreciation.
• The average return above variable cost -- also called net return or gross margin -- then is calculated by subtracting the variable costs from the gross total value product.
• These average net returns then can be compared between technologies, Some scientists believe that the return for a new technology must be at least 30 percent higher than that for the traditional technology before farmers will be willing to consider adopting it [Zandstra et al, 1981: p. 63].

This analysis generally is done on a per hectare basis, and the return calculated is a return to management, assuming that land is fixed and that labour has been valued at the price of its best alternative use (i.e., opportunity cost).

In order to maximize profits, it is necessary to maximize returns to the most limiting resource. For example, land may not be the most limiting resource, The most limiting resource may be traction time or labour for ploughing or weeding. When the most limiting resource is known, it is possible to calculate an average net return to that resource for the different technologies being compared and choose the most favourable technology. This procedure will not maximize the returns, because it does not examine different levels of inputs, but it will maximize the returns to the most limiting factor for a fixed level of inputs. For example, it is possible to calculate a return to weeding labour by omitting weeding labour costs (i.e., all other labour costs are included) from the cost total and then dividing the return by the number of hours of weeding labour, giving a return per hour of weeding labour.

10.5.3 Partial Budgeting

The partial budget is a way of analysing differences in costs and benefits of two or more competing enterprises or technologies. A good start for any economic analysis of a technology, including budgeting exercises, is to make a statement of the farmers' objectives, especially as they relate to the particular farm enterprise/technology.

The second step in any budgeting exercise is to make a detailed description of the technology or enterprise. For partial budgeting, it is necessary to be concerned only with those things that change from the existing technology to the proposed one. For budgeting in general, this step includes:

• Determining the 'unit of analysis'. The unit of analysis may be for an enterprise or technology, such as corn and bean production using an intercrop system from one hectare for one crop season, or the meat production from 100 goats for a year.
• Identifying of all operations that will be performed. For crops, this includes land preparation, tillage operations, applications of pesticides, fertilizers, etc., and marketing activities. For animals, it will include herding labour, milking, slaughtering, marketing, etc.
• Using data, where available, estimating the quantities of inputs for each operation (i.e., labour, chemicals, etc.).
• Determining the 'field price' of purchased inputs, equipment, labour, etc.' whether it is a cash cost or an opportunity cost. Multiply the price by the number of units to get a total cost for that input,
• Using data, where available, identifying the quantities of all outputs such as grain' fodder, meat, hides, etc.
• Determining a 'field price' for all outputs, whether sold or used by the household. Multiply the price by the number of units to get a total value for the particular benefit

Partial budgeting is a method of organizing data and information about the costs and benefits from some change in the technologies being used on the farm. Thus, partial budgets are useful tools for analysing small changes in farming systems and require less information than a whole farm budget or an enterprise budget, They measure changes in income and returns to limited resources; provide a limited assessment of risk; and, through sensitivity analysis, suggest a range of prices or costs at which a technology becomes profitable,

Partial budgets are not used to estimate the total income and costs for each of the technologies being considered. The goal is to estimate the difference in benefits or losses expected from the technologies. The partial budget technique is most useful where the new technology consists of the existing technology with one or two changes. The following steps are used in creating a partial budget:

• Identify the elements in the production process that are different (i.e., such as purchased inputs, different labour requirements, etc.).
• Quantify inputs that are different for the technologies.
• Calculate a 'field value' (i.e., quantity used times field price) for each input.
• Also identify, quantify, and value outputs that are different.
• Organize this information as outlined in Table 10.2, resulting in a presentation of information such as is given in Table 10.3,

To interpret this partial budget (Table 10.3), note that the increase in benefits more than makes up for the reductions in benefits, Even though more costs are associated with the new technology, there is a net gain of almost P20 per hectare from using the new technology (i.e., double ploughing) over the traditional (i.e., single ploughing) technology. In its most simple interpretation, it does pay to switch from single ploughing to double ploughing in conditions similar to those where the tests took place. The change (i.e., increase) in benefits was larger than the change (i.e., increase) in costs necessary to produce that benefit.

It is also possible to look at changes that are likely to occur if prices, yields, or input requirements change. In other words, it is possible to investigate how sensitive the results are likely to be to such changes -- a primitive measure of risk. This is done easily by setting the partial budget up on a spreadsheet such as Lotus 123. Table 10.3 shows the results that will occur if the value of labour increased or decreased by 50 percent, and what the wage rate would need to be if no increase occurred in net return from double ploughing compared with single ploughing.

TABLE 10.2: PARTIAL BUDGETING FORMAT

Source: Based on Boehlje and Eidman [1984: p. 237]

A partial budget is easy to interpret. However, it rarely is presented with a statement of the farmer's objectives, the farmer's resource base, and important non-cash considerations. A first consideration should be the question: does the criterion of increasing net benefit per hectare imply that it is in the farmer's interest to maximize returns to land? Often, this is not the case. The partial budget does not indicate if the draught and labour are available to the farmer to do a second ploughing, or if the farmer has the capital to hire a second ploughing, if ploughing is done through a hire arrangement. For many farmers, the availability of labour or capital may be more constraining than the availability of land. As has been repeatedly emphasized, the evaluation criterion used should maximize returns to the most constraining variable.

Although it a useful technique, there are limits to its value. Three problems that partial budgets do not address directly are:

• While a partial budget gives an indication of which of two or more technologies is 'better', it does not answer the question as to which technology is 'the best'.
• The partial budget may indicate that the new technology is 'better' than the traditional technology, but it will not show that both technologies produce a loss. An enterprise budget would show this type of information.
• The partial budget may indicate that a new technology is 'better' than an existing technology, but not the 'best' level at which to use the new technology. For example? feeding a supplement to dairy cattle is better than feeding no supplement, but partial budgeting will not tell how much supplement is most profitable to teed.

TABLE 10.3: PARTIAL BUDGET: DOUBLE PLOUGHING INSTEAD OF SINGLE PLOUGHING A SINGLE HECTARE, MAHALAPYE AND FRANCISTOWN AREAS, BOTSWANA, 1983-87

Source: Worman [1987]. Tables 10.4 to 10.6 are also derived from the same source.

10.5.4 Risk Analysis

Unfortunately, not all inputs are under the farmer's control. In Botswana, one of the most critical inputs for rainfed agriculture is soil moisture. There is a great deal of variability in rainfall between years, within years, and even between plots in the same village. This highly variable rainfall introduces a great deal of risk and/or uncertainty into the farming system. There may be other sources of risk as well, such as uncertainty about the availability of seed or traction. Farmers consider risk in their farming system and make adjustments to compensate. In Botswana, one of the traditional methods of addressing risk is to plant a large number of plots over a period of time in order to take advantage of the rains that do come. Another risk-reducing mechanism is to keep the investment of labour and cash in arable agriculture as low as possible. Thus, the farmer will not weed a plot if he/she thinks that plot will fail.

By looking at the distribution (i.e., range) of returns from experiments, it is possible to obtain some information on how risky a particular technology may be. There are several simple tools for looking at the distribution of returns, which can help in examining the question of risk. A very primitive method was indicated in the preceding section. It was shown that partial budgets also can be used to assess risk when a sensitivity analysis is included to show expected net gains from changes in levels of certain variable inputs/outputs and/or their prices (see Table 10.3).

In the real world, no two farmers have the same attitude toward risk. Some are more inclined to take risky actions (e.g., before, during, or after the cropping season) with the ultimate hope of receiving a larger return on their investment of labour and cash. Some are less inclined to do so. When one interprets farmers' risk attitudes, it is important to assume that they realize a trade-off may exist between getting a greater return from a technology and assuring stability in the return (i.e., that there are few years with little or no return).

Farmers generally are grouped into three categories in relation to their attitudes toward risk.

• Risk Averse. Farmers who are willing to sacrifice high expected yields and returns in order to keep the risk of low income and loss at a minimum. This type of farmer is conservative in his/her approach to decision making.
• Risk Preferring. Farmers who will go after the highest yields and expected returns, even though there is a good possibility that they will receive a low income or suffer a loss in many years. This type of farmer is liberal in his/her approach to decision-making.
• Risk Neutral. Farmers who choose the technology with the highest expected yield or return, without considering the potential for losses.

Farmers may fall into different groups depending on the season, the amount of resources available at that particular time, the magnitude of the cost, and the potential gains and losses from a given technology. When FSD workers interpret risk for technologies, it is important to remember that farmers will look at risk differently and so may fall into different recommendation domains based on their attitude towards the risk involved in a particular technology. However, in general, particularly with reference to major enterprises, limited- resource farmers in low income countries are likely to have a risk-averse attitude.

Generally, a farmer will have some idea of the risk of crop failure under the cropping system he/she currently uses. When considering a shift to a different cropping system, the farmer usually thinks not only of the cost of additional inputs (i.e., labour and cash) and the potential net gain in returns (i.e., whether considered in terms of yield or cash value), but also the increased or decreased risk of having a crop failure. If farmers think that risk will be increased under a new technology, they often will want to see a larger net gain from the shift to compensate for the increased risk. An increase in yield or net returns of 30 percent over the existing technology is probably the minimum increase that most farmers will consider acceptable before they adopt the technology. The opposite is also true. A perceived decrease in risk will often induce farmers to shift technologies, even if the gains from doing so are small.

Actual measurement of farmers' risk attitudes is very complex and not practical, because the attitudes are always changing, Thus, methods for estimating risk are based on the actual data collected in trial work, rather than trying to measure farmers' attitudes toward risk,

Two simple ways of analysing risk that depend on the outcomes from trials comparing new technologies to existing technologies are as follows:

• Safety-First Analysis. Most farmers are concerned not only with the highest yield, or return, they can get from a technology but also with the possibilities of getting little or no yield or return and, in fact, suffering a loss. Safety-first analysis looks at the results of trials comparing a new technology and a traditional technology and asks which technology will meet a chosen minimum standard -- a minimum yield or a minimum income -- most often. Alternatively, safety first can indicate which technology will produce a loss the least often. For some farmers, it is very important to have a certain amount of grain produced or the family will go hungry. For this type of farmer, it does not matter how high a yield may be possible with a new technology; if his/her family will go hungry more often under the new technology than under the old, his/her family will not switch.

Strictly speaking, a safety-first analysis is the probability (i.e., percentage of the time) that a technology exceeds a specified output level. Two or more technologies can be compared, and the one that produces at least the desired output level the highest percentage of the time is preferred. A second way of looking at the same issue is to determine the percentage of the time that a technology falls below a specific minimum level that must be reached. Table 10.4 provides some data on the percentage of times that some factors, considered in singleploughing and double-ploughing trials, were above the specified minimum. It appears that double ploughing was generally better in this measure of risk than single ploughing.

• Other Distributional Analysis. Although the safety-first risk-analysis method uses the distributions of trial outcomes in examining risk' it is also helpful to look at some of the traditional measures of distributions to get clues as to how the technologies will be assessed for risk. The traditional way of reporting trials data is to look at the arithmetic mean. The technology with the greater mean yield generally is preferred. However, one year of very good results, combined with several years of very poor results, may give a higher mean than several years of average results. And a farmer may prefer to have a steady return rather than one that fluctuates a great deal. Thus, it may help to look at the data in more depth.

One way to provide more information to the farmer is to compare the median, which is the value that is half way between the highest and the lowest value, with the mean. If the median is less than the mean, it indicates that the farmer will receive less than the mean return more than half of the time. It may be helpful to look at how much difference there is between the mean and the median. If there is little difference, then the farmer will receive close to the mean most of the time. A lot of difference may indicate a few really good returns for the technology and many rather poor ones. Actually graphing the distribution can help in determining what is happening in the relation of means and medians. These are some indications of the skewness of a distribution.

Another potentially useful measure is the percentage of the time that a farmer can expect to receive a yield or return less than the mean yield or return. If this percentage is high, it generally indicates a situation of a few good and many poor yields or returns. Table 10.4 provides information on the mean, median, and percentage of times the yield or return was less for some single-ploughing and double-ploughing data. Although, in general, both the mean and the median were greater for the double ploughing, indicating a possible overall advantage, the percentage of the time that single ploughing was less than the mean was smaller than for double ploughing, indicating that double ploughing may be somewhat less stable.

FSD workers may want to consider using an average of the five lowest yields rather than just the lowest yield for comparison purposes. In the case of the data from Table 1().4, the lowest yield for both single ploughing and double ploughing was zero (i.e., no yield). This produced net losses of P2.70 for single ploughing and P5.70 for double ploughing. Comparing an average of the five lowest yields gave average of 1.8 kilograms/hectare for single ploughing (i.e., average return of minus P 1 .97), whereas the five lowest yields for the double ploughing averaged 19.4 kilograms/hectare (i.e., average return of P2.90). This implies, that all other things being equal, farmers may prefer the double-ploughing technology. This method is sometimes called minimum returns analysis [CIMMYT, 1988A].

TABLE 10.4: SOME INDICATORS OF RISK, RMFI DOUBLE-PLOUGHING TRIALS, FRANCISTOWN, BOTSWANA' 1985-87

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