Search results
Agricultural experiments have to deal with long-term effects of cropping practices. Think about fertilisation: certain types of organic fertilisers may give effects on soil fertility, which are only observed after a relatively high number of years (say: 10-15).
ANOVA is particularly useful in cases where a researcher might be evaluating the effect of fertilizer rate across several varieties of crop. Here, the researcher is interested in whether varieties differed in response to fertilizer, which will be exposed through a significant interaction term in the ANOVA source table.
There are many examples in agronomy and weed science where mixed effects models are appropriate. In ANOVA, everything except the intentional (fixed) treatment (s), reflect random variation.
For example, an experiment with several different varieties of a crop and several different fertilizer treatments could use main plots for the fertilizer treatments and sub-plots within main plots for the individual varieties.
Let’s develop an example to see how an ANOVA is done. An experiment in northwest Iowa compared the yield of a corn hybrid planted at three plant densities to determine the optimum planting rate. The data are given in Table 1.
Consider the example in Figure 1, where hypothetical data for tomato plants receiving different types of fertilizers have been plotted. The results indicate that fertilizers 3 and 6 were significantly better than fertilizer 2 and 5 but were not statistically different than fertilizers 1 and 4. Fert 1.
Completely Randomized Design. The completely randomized design works best in tightly controlled situations and very uniform conditions. A farmer wants to study the effects of four different fertilizers (A, B, C, D) on corn productivity. Three replicates of each treatment are assigned randomly to 12 plots.
