Prediction of survival of Pinus radiata seedlings subjected to physical-water restriction extreme using learning neural networks

Abstract

Drought is one of the main environmental factors that limit plant growth. For this reason, it is necessary to apply nursery cultural practices to produce quality seedlings for successful reforestation in drought- prone sites. In this study, the extreme learning machines and multilayer are applied to predict survival in 5-month-old Pinus radiataseedlings belonging to 98 families of a genetic improvement program and subjected to a period of water restriction in the nursery. After applying the water restriction, survival was registered in each seedling as a categorical variable (1 = alive seedling, 0 = dead seedling). Additionally, the following morphological attributes of each seedling were also measured: total height, root collar diameter, slenderness index, dry weight of needles, stems and roots, total dry weight, and the root to shoot ratio. The extreme learning machines predicted with a better rate the survival of the “alive” class compared to the “dead” class. On the other hand, the multilayer-extreme learning machines improved the precision of survival concerning the class of “dead” seedlings. According to the results of the model, an overall precision of 74% was obtained. This may be due to the great genetic variability presented by each of the Pinus radiatafamily used in the database. However, this technique allowed predicting the survival of a group of seedlings grown in the nursery, which can be a tool to support the selection process of high quality planting stock.

Existence of solutions for an optimal control problem in forestry management

“…The extraction rate of forest plantations is a social and economic decision within certain biological and physical constraints [1,2]. Plantation forestry for industrial purposes was not a priority until the mid-20th century in countries such as Australia, New Zealand, the United States, and Chile [3].…”