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Dendrometric analysis of olive trees for wood biomass quantification in Mediterranean orchards
Authors:Borja Velázquez-Martí  I López Cortés  D M Salazar-Hernández
Institution:1. Departamento de Ingeniería Rural y Agroalimentaria, Universidad Politecnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain
2. Departamento de Producción Vegetal, Universidad Politecnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain
Abstract:This work focuses on the development of dendrometric algorithms to calculate the volume and total biomass contained in olive trees. This laid the foundation for the use of this methodology as a tool to manage resources from orchards, establishing adequate prediction models for assessing other parameters such as income from raw materials from the cultivation, fruit production, CO2 sinks, and waste materials (residual wood) used for energy or industry. Dendrometry has traditionally been applied to forest trees. However, little research has been conducted on fruit trees because of their heterogeneous structure. This issue was the first step of this research. For this, the form factors were calculated. This relates to the actual volume of the branch with a model volume, calculated as a revolution solid from the base diameter and length. The shape more approximated to 1 was the cylinder model with a mean value of 0.76 and standard deviation (SD) of 0.23. On the other hand, volume equations were obtained for the branches. The distribution of biomass in the tree was analyzed. It is estimated that 40 % of biomass is located in the stem and 60 % in the crown, and most of the crown biomass is concentrated in the first branches (60 %). Afterwards, occupation factors were calculated to relate the wood volume in the crown to its apparent volume, the mean being 0.005 dm3/m3 and SD 0.0025 dm3/m3. Also, equations for predicting the whole wood in the crown were obtained. In this regard, the best results were obtained when the crown diameter was used (R 2 = 0.74). These results could be correlated with the production and quality of the fruit, amount of residual biomass coming from pruning, and LIDAR data, which may indicate a simple, quick, and accurate method for predicting biomass.
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