Contents & Foreword, Characterizing And ... - IRRI books

Interpreting the results of independence tests and cluster analysis also presentsdifficulties. While independence tests indicate that late-maturing varieties discourageweeds better than early maturing varieties (Table 4), correspondence analysis resultedin the formation of domain A (Table 9), which contains traditional varieties, low yields,and higher than average weed levels. In other words, fields with traditional varietiesand high weed levels had lower yields, but this does not mean that traditional ricevarieties encourage weeds. On the contrary, laboratory screening has revealed that atleast 11 traditional Cambodian rice cultivars exhibit allelopathic properties againstawnless barnyard grass, Echinochloa colona (L.) Link, while the IR varieties testeddo not exhibit such traits (Pheng et al 1999).This assessment of pest constraints to RLR production did not include data onthe soil types, nor did we analyze the data in terms of relative amounts of NPK orother minerals applied. The effect of soil nutrient stress on pest damage may be indirect,but profound. For example, nutrient stresses may delay crop development (Kirket al 1998), resulting in crops that are out of synchrony with crops in the surroundingarea, and therefore at greater risk of pest damage. Detailed soil and fertilizer variableswere recorded and taken into account in the follow-up study designed for multipleregression analysis, described above (CIAP 1998, 1999).Another limitation of this technique arises from the assessments of injuries orpest levels over four successive development stages. There are three ways to dealwith the large data set of multiple pests spread over time. First, the entire data setcould be analyzed: carrying out the analysis for each pest for each stage. Not onlywould this process be time-consuming, but the results would be difficult to interpret,since different pests affect the crop at different stages. For instance, an analysis oftillering-stage pests may reveal the relationship of gall midge and stem borer to yield,but not rice bugs, which are primarily pests at the milk stage. Analyzing all of the databy stage would not reveal whether rice bugs or gall midges represent a greater constraintto RLR production.A second method would be to compact the data over time: creating a new variablebased on the area under a curve or an average of the pest/injury levels at eachcrop stage (Jahn 1992, Savary et al 1996, Teng and Bissonnette 1985). Taking thearea under the curve would be quite misleading for some of the pest variables, however.Some types of damage are retained from one stage to the next. Tillers damagedby gall midge at the tillering stage will still be visible at the booting stage, but notvisible by the milk stage (Fig. 2). Taking the area under the gall midge curve wouldlead to an exaggeration of the amount of gall midge damage, since most of the gallsrecorded at tillering would be recorded again at booting. Other types of injury, suchas whitehead resulting from stem borer damage, only appear at a certain stage. Thearea under the curve for whitehead (Fig. 2) would include the large area created byconnecting the data point, i.e., zero, at booting to the data point at the milk stage,when in fact no whitehead is visible before the milk stage. An additional problem isthat the area under the curve would depend on the graphed distance from one stage toanother. Since the study included numerous rice varieties, the number of days betweenstages varies greatly. Attempting to incorporate that information into the analy-Characterizing biotic constraints to production . . . 263