Factorial and split-plot designs are two commonly used experimental designs in agricultural research that can be applied to multiple environments. These designs are particularly useful for studying the effects of two or more factors on a response variable in a systematic and efficient manner.
A factorial design involves manipulating two or more factors simultaneously, and measuring their effects on a response variable. For example, in an agronomic study, one might investigate the effects of two factors - fertilization and irrigation - on the yield of a particular crop. The levels of fertilization and irrigation can be varied independently, and the yield of the crop can be measured for each combination of treatments.
A split-plot design, on the other hand, involves dividing the experimental units into smaller plots, and applying different treatments to each plot. For example, in an agronomic study, one might investigate the effects of two factors - planting density and weed control - on the yield of a particular crop. The planting density might be varied at the main plot level, while weed control treatments might be varied at the sub-plot level within each main plot.
When applied to multiple environments, factorial and split-plot designs can be used to evaluate the effects of different treatments under different environmental conditions, and to identify treatments that are effective across multiple environments. For example, in a study of crop yield under different environmental conditions, a factorial design might be used to investigate the effects of fertilization and irrigation on yield across multiple locations and years, while a split-plot design might be used to investigate the effects of planting density and weed control under different soil types and weather conditions.
In agronomy, a suitable example of a factorial design applied to multiple environments is a study on the effects of nitrogen and phosphorus fertilization on the yield of maize across different soil types and climatic conditions. The levels of nitrogen and phosphorus can be varied independently, and the yield of maize can be measured for each combination of treatments across multiple locations and years. This design allows researchers to identify the most effective combination of nitrogen and phosphorus fertilization for different soil types and climatic conditions, and to determine whether the effects of these factors on yield are consistent across different environments.
In a split-plot design, an example in agronomy could be a study on the effects of planting density and herbicide application on the yield of soybeans in different regions with varying weed pressures. The planting density could be varied at the main plot level, while the herbicide treatments could be varied at the sub-plot level within each main plot. This design allows researchers to evaluate the effects of different herbicide treatments under different weed pressures and to identify the most effective planting density for different regions.
Here is an example of an experiment in which 6 Nitrogen levels for the main plot and two varieties for the sub-plot were grown in three environments in three replications. The arrangement of the data is given below:
Site Nitrogen Variety Rep Yield
1 1 1 1 1979 1 1 1 2 1511 1 1 1 3 3664 1 1 2 1 5301 1 1 2 2 1883 1 1 2 3 3571 1 2 1 1 4572 1 2 1 2 4340 1 2 1 3 4132 1 2 2 1 5655 1 2 2 2 5100 1 2 2 3 5385 1 3 1 1 5630 1 3 1 2 6780 1 3 1 3 4933 1 3 2 1 6339 1 3 2 2 6622 1 3 2 3 6332 1 4 1 1 7153 1 4 1 2 6504 1 4 1 3 6326 1 4 2 1 8108 1 4 2 2 8583 1 4 2 3 7637 1 5 1 1 7223 1 5 1 2 7107 1 5 1 3 6051 1 5 2 1 7530 1 5 2 2 7097 1 5 2 3 6667 1 6 1 1 7239 1 6 1 2 6829 1 6 1 3 5874 1 6 2 1 7853 1 6 2 2 7105 1 6 2 3 7443 2 1 1 1 3617 2 1 1 2 3580 2 1 1 3 3939 2 1 2 1 3447 2 1 2 2 3560 2 1 2 3 3516 2 2 1 1 6065 2 2 1 2 5463 2 2 1 3 5435 2 2 2 1 5905 2 2 2 2 5969 2 2 2 3 6026 2 3 1 1 6092 2 3 1 2 6571 2 3 1 3 6084 2 3 2 1 5322 2 3 2 2 5883 2 3 2 3 6489 2 4 1 1 5916 2 4 1 2 6982 2 4 1 3 7145 2 4 2 1 6513 2 4 2 2 6556 2 4 2 3 7853 2 5 1 1 7191 2 5 1 2 6109 2 5 1 3 7967 2 5 2 1 8153 2 5 2 2 7208 2 5 2 3 6685 2 6 1 1 5805 2 6 1 2 6890 2 6 1 3 7113 2 6 2 1 7290 2 6 2 2 6564 2 6 2 3 7401 3 1 1 1 4320 3 1 1 2 4068 3 1 1 3 3856 3 1 2 1 4891 3 1 2 2 2577 3 1 2 3 4062 3 2 1 1 5751 3 2 1 2 5635 3 2 1 3 4658 3 2 2 1 5684 3 2 2 2 5902 3 2 2 3 5459 3 3 1 1 5031 3 3 1 2 5685 3 3 1 3 5420 3 3 2 1 5068 3 3 2 2 5020 3 3 2 3 5573 3 4 1 1 5985 3 4 1 2 6295 3 4 1 3 6544 3 4 2 1 6023 3 4 2 2 5964 3 4 2 3 6291 3 5 1 1 6194 3 5 1 2 6430 3 5 1 3 6046 3 5 2 1 6923 3 5 2 2 6140 3 5 2 3 5954 3 6 1 1 5987 3 6 1 2 6582 3 6 1 3 6734 3 6 2 1 7004 3 6 2 2 7221 3 6 2 3 6339Copy Data
This data arrangement ensures that the two-factor analysis is accurate and consistent. Only numeric values will be processed for the analysis.
This module allows users to analyze two-factor experimental designs over multiple environments. Users can select the design type and comparison method for their analysis. The analysis requires input for the number of variables (characters), the number of environments (locations), levels of factors, and replications.
Once all fields are completed, click the “Analyse” button. The system will process the input and provide the analysis results based on the selected design and comparison method.