Why conduct experiments?...

1. Why conduct experiments?... • To explore new technologies, new crops, and new areas of production • To develop a basic understanding of the factors that control production • To develop new technologies that are superior to existing technologies • To study the effect of changes in the factors of production and to identify optimal levels • To demonstrate new knowledge to growers and get feedback from end-users about the acceptability of new technologies

2. What is a designed experiment? • Treatments are imposed (manipulated) by investigator using standard protocols • May infer that the response was due to the treatments Potential pitfalls • As we artificially manipulate nature, results may not generalize to real life situations • As we increase the spatial and temporal scale of experiments (to make them more realistic), it becomes more difficult to adhere to principles of good experimental design

3. What is an observational study? • Treatments are defined on the basis of existing groups or circumstances • Uses • Early stages of study – developing hypotheses • Scale of study is too large to artificially apply treatments (e.g. ecosystems) • Application of treatments of interest is not ethical • May determine associations between treatments and responses, but cannot assume that there is a cause and effect relationship between them • Testing predictions in new settings may further support our model, but inference will never be as strong as for a designed (manipulative) experiment

4. Some Types of Field Experiments(Oriented toward Applied Research) • Agronomy Trials • Fertilizer studies • Time, rate and density of planting • Tillage studies • Factors are often interactive so it is good to include combinations of multiple levels of two or more factors • Plot size is larger due to machinery and border effects • Integrated Pest Management • Weeds, diseases, insects, nematodes, slugs • Complex interactions betweens pests and host plants • Mobility and short generation time of pests often create challenges in measuring treatment response

5. Types of Field Experiments (Continued) • Plant Breeding Trials • Often include a large number of treatments (genotypes) • Initial assessments may be subjective or qualitative using small plots • Replicated yield trials with check varieties including a long term check to measure progress • Pasture Experiments • Initially you can use clipping to simulate grazing • Ultimately, response measured by grazing animals so plots must be large • The pasture, not the animal, is the experimental unit

6. Types of Field Experiments (Continued) • Experiments with Perennial Crops • Same crop on same plot for two or more years • Effects of treatments may accumulate • Treatments cannot be randomly assigned each year so it is not possible to use years as a replication • Large plots will permit the introduction of new treatments • Intercropping Experiments • Two or more crops are grown together for a significant part of the growing season to increase total yield and/or yield stability • Treatments must include crops by themselves as well as several intercrop combinations • Several ratios and planting configurations are used so number of treatments may be large • Must be conducted for several years to assess stability of system

7. Types of Field Experiments(Continued) • Rotation Experiments • Determine effects of cropping sequence on target crop, pest or pathogen, or environmental quality • Treatments are applied over multiple cropping seasons or years, but impact is determined in the final season • Farming Systems Research • To move new agricultural technologies to the farm • A number of farms in the target area are identified • Often two large plots are laid out - old versus new • Should be located close enough for side by side comparisons • May include “best bet” combinations of several new technologies • Recent emphasis on farmer participation in both development and assessment of new technologies

8. Choice of Experimental Site • Site should be representative • Grower fields may be better suited to applied research • Suit the experiment to the characteristics of the site • make a sketch map of the site including differences in topography • minimize the effect of the site sources of variability • consider previous crop history • if the site will be used for several years and if resources are available, a uniformity test may be useful

9. Greenhouse effects • Greenhouse and growth chambers are highly controlled, but in practice may be quite variable • Not representative of field conditions • light • growth media • unique insect pests and diseases • Experiments can be conducted in the off-season

10. Experimental Error • Modern experimental design should: • provide a measure of experimental error variance • reduce experimental error as much as possible Variation between plots treated alike is always present

11. Natural sources of error in field experiments • Plant variability • type of plant, larger variation among larger plants • competition, variation among closely spaced plants is smaller • plot to plot variation because of plot location (border effects) • Seasonal variability • climatic differences from year to year • rodent, insect, and disease damage varies • conduct tests for several years before drawing firm conclusions • Soil variability • differences in texture, depth, moisture-holding capacity, drainage, available nutrients • since these differences persist from year to year, the pattern of variability can be mapped with a uniformity trial

12. Uniformity Trials • The area is planted uniformly to a single crop • The trial is partitioned into small units and harvested individually • Adjustments are made to distinguish patterns in the data from random noise • Areas of equal yield are delineated

13. Interpretation • Determine suitability of the site for the experiment • uniformity critical for fertility trials • Make decisions concerning management of site over time • cover crops • Group plots into blocks to reduce error variance within blocks • blocks do not have to be rectangular • Determine size, shape and orientation of the plots

14. Uniformity trials? • costs • time constraints • land limitations • pressure to publish or perish • may already have knowledge of field characteristics, previous cropping history • new technological tools may achieve the same or better result

15. Precision Agriculture Techniques, technologies, and management strategies that address within-field variability of parameters that affect crop growth. • soil type • soil organic matter • plant nutrient levels • topography • water availability • weeds • insects

16. Tools of Precision Agriculture • GPS and GIS – constant reference to geographic coordinates • Remote Sensing – infrared maps • Equipment such as combines that can continuously monitor yield at harvest • Crop Modeling • Spatial analyses

17. Example: central Missouri farm Aerial photograph, soil pH and 3-year average grain yields Source: http://muextension.missouri.edu/explore/envqual/wq0450.htm

18. Spatial Analyses • Utilize patterns in the data to adjust for heterogeneity in an experiment • Example: ASReml http://www.vsni.co.uk/software/asreml Not a substitute for good experimental design and technique!