## Number of Plots

Deciding on the number of plots to be measured or monitored is a critical step in assessing carbon stock changes in a land-use category or project. There is a trade-off between precision and cost. The more spatially variable the carbon stocks in a project, the more sampling plots needed to attain a given level of precision at the same confidence level (IPCC 2003). This has cost implications for planning and implementing a monitoring plan. The number of sample plots should be chosen with statistical rigour to get a correct assessment of the impact of a land-based project on carbon stocks, roundwood production or soil organic matter. The number depends on the desired precision, size of the project, variation in vegetation parameters, available budget and cost of monitoring. Sampling methods and procedures can be complex, forcing many researchers to adopt a standard number of plots based on the visible heterogeneity of vegetation, soil and other conditions. There are a number of statistical approaches to and formulae for determining sample size (Usher 1991; Kangas and Maltamo 2006). A commonly adopted approach is illustrated here. The following steps and calculations are necessary for arriving at the number of sample plots:

Step 1: Define the desired precision level.

Step 2: Estimate the variance.

Step 3: Estimate the cost of estimation or monitoring.

Step 4: Define the permissible error.

Step 5: Define the confidence interval.

Step 6: Determine the number of strata.

Step 7: Estimate the number of plots.

Step 1: Define the desired precision level Typically, to estimate the number of plots needed for measuring and monitoring at a given confidence level, it is necessary to first estimate the variance of the variable (e.g. carbon stock of the main pools, trees in an afforestation or reforestation project or soil in a cropland management project) in each stratum (IPCC 2003). This can be accomplished either by using existing data from a project similar to the one yet to be implemented (e.g. a forest or soil inventory in an area representative of the proposed project) or by conducting pilot study from an area representative of the proposed project.

Carbon inventory requires reliable estimates, which means the values are precise and accurate. Higher the level of precision, larger the sample size and higher the cost. The level of precision should be determined at the beginning of the project, which could vary from ±5% to ±20% of the population mean. However, a precision level of ±10% of the true value of mean at a confidence interval of 95% is normally adequate, although a range of ±5% or even ±20% is also often employed.

Step 2: Estimate the variance An estimate of variance of the carbon stocks is required for each stratum, which could be obtained from studies conducted in a region with conditions similar to those for each proposed project activity. If such estimates are not available, pilot studies may be required in locations close to the project area. Such a study involves the following steps:

• Identify an area near the project area with conditions similar to those for the proposed project activities (e.g. tree plantation, agroforestry or protected forest).

• Conduct field studies by selecting a few small sample plots in the selected vegetation type. Measure the relevant tree or non-tree parameters such as DBH, tree height and weight of shrub or herb biomass (as described later in the chapter as well as in the following chapters).

Calculate the mean and variance from the data collected from the pilot study.

Step 3:

Step 4:

Step 7:

Obtain cost estimates for monitoring While conducting the preliminary field studies, keep notes of all the costs involved in traveling, laying plots, making measurements and calculations and any other expenses. Using these values, estimate the cost of sampling a plot in a given stratum. If cost figures are available from other similar studies, use them.

Permissible error Estimate the permissible error in the mean carbon stock value estimates. Usually, the permissible error value can be taken as ±10% of the expected mean carbon stock.

Confidence interval Choose a value of 95% confidence level.

Number ofstrata Select the number of strata for the project activity (refer to

Section 10.3).

Estimate number of plots Calculate the required number of plots using the following formulae: 