• Key message

Errors in forest stand attributes can lead to sub-optimal management prescriptions concerning the set management objectives. When the objective is net present value, errors in mean diameter result in greater losses than similar errors in basal area, and underestimation greater losses than overestimation.

• Context

Errors in forest inventory data can cause inoptimality losses in the objectives set to forest management. Losses occur when the forest is treated with management prescriptions that are optimal for erroneous data but not for correct data.

• Aims

We evaluate the effect of varying levels of errors in basal area and mean diameter on the inoptimality losses.

• Methods

Errors from 20% of overestimation to 20% of underestimation were simulated in basal area and mean diameter. For each stand, the management prescription that maximized the net present value was selected with and without errors. The inoptimality losses were calculated for different error levels.

• Results

The tested error levels resulted in inoptimality losses of 0.11–3.01%. Errors in mean diameter increased inoptimality losses more than similar relative errors in basal area. Simultaneous underestimation of basal area and mean diameter led to greater inoptimality losses than simultaneous overestimation of these attributes.

• Conclusion

If the forest is considered as an investment, using inventory data where basal area and mean diameter are underestimated causes greater losses compared with data where these attributes are overestimated. Errors in mean diameter are more important than similar errors in the basal area. Large errors in basal area and mean diameter should be avoided especially in stands where the basal area is high.

中文翻译：

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基准面积和平均直径的误差对森林经营处方最佳化的影响

• 关键信息

林分属性的错误可能导致与设定的管理目标有关的次优管理规定。当目标是净现值时，平均直径的误差会导致比基面积的类似误差更大的损失，而低估的损失会大于高估。

• 语境

森林清单数据中的错误可能会导致为森林管理设定的目标失去最优性。当使用对错误数据最佳而不对正确数据最佳的管理规定来处理森林时，就会发生损失。

•目的

我们评估了基面积和平均直径的误差水平的变化对不最优损失的影响。

• 方法

从基础面积和平均直径模拟了从高估20％到低估20％的误差。对于每个摊位，选择有无错误的使净现值最大化的管理处方。针对不同的误差水平计算了非最优损失。

• 结果

经测试的错误级别导致0.11-3.01％的最优性损失。平均直径的误差增加了不最优性的损失，比基底面积的类似相对误差更大。与同时高估这些属性相比，同时低估基础面积和平均直径会导致更大的最优性损失。

• 结论

如果将森林视为一种投资，则与低估了这些属性的数据相比，使用基本面积和平均直径被低估的清单数据会造成更大的损失。平均直径的误差比基底区域的类似误差更重要。应避免在基础面积和平均直径方面出现较大误差，尤其是在基础面积较大的林分中。