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