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multipurpose tree species research for small farms: strategies ... - part

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Planning<br />

Diagnosis is only useful if translated into a plan<br />

of action. Reliance on subjective opinion to<br />

determine <strong>research</strong> or extension priorities does not<br />

naturally lead to such a plan. Researchers tend to<br />

accord priority to those problem areas with which<br />

they are familiar. Without an objective means of<br />

ranking, the obvious problem, such as a large insect<br />

chewing its way through maize leaves, is accorded<br />

more importance than a less noticeable but more<br />

serious problem, such as soil erosion and long term<br />

loss of fertility. It is often not clear whether<br />

problems require <strong>research</strong> or extension.<br />

Tripp and Woolley (1987) have recently<br />

developed a more detailed planning procedure,<br />

Their method, adaptcd slightly, involves scoring<br />

and ranking problems on the basis of:<br />

- the loss in productivity (%) either at present or<br />

likely in the future;<br />

- the importance of the enterprise (crop or<br />

livestock) to individual farmers (area or number<br />

per farm, value, food security);<br />

- frequency of occurrence; and<br />

- number of farmers affected,<br />

Solutions to these problems can then be<br />

prioritized on the basis of potential benefit -productivity,<br />

stability, sustainability, equitability;<br />

and ease of adoption -- compatibility with farming<br />

system, complexity, amount of inputs/credit needed.<br />

The confidence which <strong>research</strong>ers and<br />

extensionists have in the problems and proposed<br />

solutions should then determine the appropriate<br />

<strong>for</strong>m of action (Figure 1). If the problem is not<br />

clearly defined, more diagnostic work needs to be<br />

done. If the best solution to the problem is not<br />

obvious, <strong>research</strong> needs to be carried out to<br />

evaluate a number of hypothes :ed options. If<br />

there is agreement about the best solution, but<br />

experience with implementation is lacking,<br />

verification of the technology is needed. If there is<br />

confidence that the technology is suitable <strong>for</strong><br />

farmers, then time and money should not be<br />

wasted with <strong>research</strong> -- the technology should be<br />

implemented.<br />

Searching <strong>for</strong> possible solutions is heavily<br />

dependent on experience, and can not simply be<br />

taught. As Huxley (1987) stated in relation to<br />

agro<strong>for</strong>estry, it should be clear what is already<br />

known and what is not. Un<strong>for</strong>tunately, in practice<br />

this is not always the case, and a considerable<br />

157<br />

degree of repetitive <strong>research</strong> is carried out.<br />

Sources of solutions include the literature,<br />

practices in other areas, practices on<br />

progressive <strong>farms</strong>, and work at other institutes<br />

and projects. Access to literature may be<br />

difficult, and much literature may be in a<br />

different language. Newsletters in dsimpler<br />

<strong>for</strong>mat can help, and computerized data bases<br />

and synthesizing models offer potential <strong>for</strong> the<br />

future.<br />

Technology testing<br />

Theory<br />

Much of the early FSR work involved<br />

developing and testing technology <strong>for</strong> annual<br />

crops. A sequence of e:periment typ s and<br />

designs led progressively from <strong>small</strong>-plot<br />

experiments on a few <strong>farms</strong>, emphasizing the<br />

evaluation of biological productivity, to larger<br />

plots on additional <strong>farms</strong> where econormic<br />

returns and farmer acceptance were evaluated<br />

(Collinson 1987).<br />

Component technology trials determine the<br />

optimum type or level of an input (variety,<br />

fertilizer, or plant arrangement). This is the<br />

testing stage in Figure 1. Typically, component<br />

technology trials use randomized, compl ete<br />

block e~perimental designs, with plot sizes of<br />

10-50m " and results evaluated by an analysis of<br />

variance of yields. Component technology trials<br />

lead to a package of recommendations.<br />

Trials to compare the economic benefit of<br />

this new technology are often called verification<br />

trials. These triaLs often consist of two<br />

treatments: improved vs. farmer-planted in plots<br />

of 250-1,000m . Farmers are responsible <strong>for</strong><br />

carrying out the operations after appropriate<br />

demonstrations. The number of farmers<br />

included in such trials has varied, with<br />

suggestions ranging from 6 (Zandstra et al.<br />

1981) to at least 30 (FSSP 1987), probably due<br />

to the degree of uni<strong>for</strong>mity of farm conditions<br />

familiar to the <strong>research</strong>ers. Analysis of the<br />

verification trial results has usually stressed a<br />

comparison of production, economic return,<br />

and variability of these across the sample<br />

(Hildebrand and Poey 1985).<br />

The need <strong>for</strong> on-farm testing of the economic<br />

and social benefits to livestock technology,<br />

compared'to further documentation of<br />

biological effects, has been increasingly stressed<br />

(Devendra 1987). Similar to crop <strong>research</strong>, the<br />

ideal approach has been stated as a progression

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