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Objectives of grazing trials parallel the objectives of grazing, which are improvement or maintenance of forage production, efficient use of forage produced, and sustained high forage and animal production. In turn, trial objectives and biological and financial constraints determine the variables to be measured and how they are to be measured, the design and analysis of the trial, and interpretation of the results.
Evaluation of forage species and management factors with grazing animals requires judicious planning and clearly stated objectives. In these macro-experiments, the cost of land, animals, and labor can restrict the objectives of the study. Further, measurements taken during a trial should not be a consequence of resources; rather, they should be selected to meet the objectives. Animal response data are collected, but frequently little or no pasture characterization occurs, so potential explanatory power of the variables that influence animal response is negated. Although measurements of animal response and pasture productivity have producer utility, they can only be discussed in vague terms and, consequently, neither knowledge nor understanding of pasture and animal interrelationships are advanced. This chapter outlines the importance of estimating herbage mass (kg ha−1) in grazing experiments. Herbage mass is one of four pasture measurements recommended in the conduct of all grazing experiments. An estimate of herbage mass provides basic information from which some understanding of the plant animal interaction can be derived. Justification for estimating herbage mass, methods of estimation, its use in reporting animal response data, and its relationships with animal response are discussed. The other three variables recommended for routine measurements are green (nonsenescent) leaf mass (kg ha−1), diet quality, and herbage desnity (kg ha−1 cm−1). Additional desirable explanatory measurements are suggested and prioritized.
Clearly defined and prioritized objectives ultimately determine the measurements required for each grazing experiment. Forage/livestock research ranges from investigation of univariate processes to entire systems of production. Resources often are limiting in forage-animal research; consequently, the experimental design and responses to be measured should be chosen carefully. Response criteria should be selected only if they are relevant to objectives, can be measured with adequate precision, and permit reasonable testing of hypotheses. If these conditions are not met, resources may be wasted.
Grazed ecosystems are characterized by a dynamic, hierarchical interaction of soil, plants, and animals. Because of continuous flux between components and the impact of each component on the other components, the plant-animal interface has largely been neglected throughout many years of research. More conventional approaches have included studying one of the components while either ignoring the others or attempting to hold them static. Also, conventional experimental approaches to evaluation of grazing lands tend to measure responses over long periods of time, at infrequent intervals, seldom less than a grazing season of 12 to 16 weeks. Typical responses measured are net production, usually weight gain, or aboveground plant biomass. Gain is influenced by many factors, both of animal and plant origin, most notably dry matter intake and digestibility. Measurements of grazing behavior, in response to changes in the sward structure, chemical characteristics, and availability, lend themselves to short-term assessment of animal response compensations when foraging is restricted. Short-term intake can be calculated from measures of grazing behavior, and inferences can be made concerning the grazing strategy of various kinds of grazers. These parameters aid in the development of ecological and production models of the grazing system.
Financial and logistical constraints usually restrict the number of animals and paddocks in grazing experiments. This forces a compromise in key elements of design, such as number of treatments, stocking rates (or levels of herbage mass if variable stocking is used), and replicates. Stocking procedures, such as stocking method (fixed vs. variable), are also important considerations in grazing experiments because they affect interpretation and application of results, and management requirements. Strengths and weaknesses of alternative designs and stocking procedures are discussed for grazing experiments with different objectives. Replication provides the best estimate of experimental error but restricts the number of treatments and/or stocking rates within constrained resources. Nonreplicated, multiple fixed, or variable stocking rate designs and partial replication constitute alternatives that may make more efficient use of restricted facilities than do traditional replicated designs, to meet specific objectives.
Research designs suitable for grazing studies are many and varied. The ability to detect treatment differences depends entirely on the model of the grazing experiment and the research design actually used. Small changes in the design can increase or decrease the sensitivity of a test by virtue of the mean square used for the error variance. Examples of experiments are evaluated and compared herein from the point of view of statistical designs and analysis. Invariably, the final design is a compromise between an ideal and the realities of budget and time constraints.
Grazing experiments have invariably represented compromises between the ideal comprehensive experiment and the small trial resulting after concessions enforced by limited resources. Because factors having major effects generally are easier to discover, it follows that as progress continues there will be increasing emphasis on detecting ever smaller effects. Without a concomitant increase in size of experiments there will be an increased need to rely on conclusions gained from combining information from independent studies. This chapter explores some of the alternative designs and suggests methods for combining information from separate experiments.
An objective for developing new designs for grazing research is to reduce sampling cost or to reduce experimental errors. Predictions from a simple time series model modified by external effects such as precipitation and forage harvested can be combined with sample results in a linear filter to achieve these goals. These improved estimates and predictions can be used adaptively to guide managers toward an optimal stocking rate under certain well-specified conditions. Economic analyses usually assume that the conditions for incremental convergence to an optimum exist; ecologists frequently cite concepts of hysteresis and discontinuity to indicate that incremental convergence to an optimal stocking rate is not possible.
The experimental design of grazing trials affects the ability to provide information relevant for economic analysis. A key management issue—optimal stocking rate on range or improved pasture—is analyzed from the perspective of both profitability and risk-return tradeoffs. To identify the most profitable grazing system, the experimental design of stocking rate studies must include a broad range of treatment levels (stocking rates) so that weight gain response can be determined. To assess the impact of weather risk, data must be collected in both good and bad years so that probabilistic estimates of weight gains and economic returns can be determined. Experiments show that (i) stocking rates that maximize average daily gain or weight gain per unit area do not necessarily maximize profits, and (ii) high stocking rates increase the exposure of the producer to risk.
We are all modelers of one type or another. Researchers who conduct grazing trials are utilizing/developing physical models that are biologically complete but mathematically incomplete. Researchers who use/develop dynamic simulations are utilizing/developing mathematical models that are mathematically complete but biologically incomplete. All models should be examined in terms of their objectives, assumptions, completeness, sensitivity, credibility, and ability to predict while recognizing that not all models are created equal. Ideally, grazing trial researchers and researchers who utilize dynamic simulation should work together in establishing a mutually beneficial grazing experiment. Researchers coordinate their efforts with statisticians to enhance the quality of the experiment. A similar procedure is recommended for the use of dynamic simulations.