Forecasting Variations in Profitability and Silviculture under Climate Change of Radiata Pine Plantations through Differentiable Optimization

Miguel A González-Rodríguez ,Miguel Ernesto Vázquez Méndez ,Ulises Diéguez-Aranda

doi:10.3390/f12070899

Miguel A González-Rodríguez , Miguel Ernesto Vázquez Méndez + Show 1 more

Open Access

https://doi.org/10.3390/f12070899

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Journal: Forests	Publication Date: Jul 10, 2021
License type: CC BY 4.0

Affiliation: University of Santiago de Compostela

Abstract

Climate change might entail significant alterations in future forest productivity, profitability and management. In this work, we estimated the financial profitability (Soil Expectation Value, SEV) of a set of radiata pine plantations in the northwest of Spain under climate change. We optimized silvicultural interventions using a differentiable approach and projected future productivity using a machine learning model basing on the climatic predictions of 11 Global Climate Models (GCMs) and two Representative Concentration Pathways (RCPs). The forecasted mean SEV for future climate was lower than current SEV (∼22% lower for RCP 4.5 and ∼29% for RCP 6.0, with interest rate = 3%). The dispersion of the future SEV distribution was very high, alternatively forecasting increases and decreases in profitability under climate change depending on the chosen GCM. Silvicultural optimization considering future productivity projections effectively mitigated the potential economic losses due to climate change; however, its ability to perform this mitigation was strongly dependent on interest rates. We conclude that the financial profitability of radiata pine plantations in this region might be significantly reduced under climate change, though further research is necessary for clearing the uncertainties regarding the high dispersion of profitability projections.

Highlights

IntroductionDeclines in forest productivity and fast changes in species suitability are among the potential negative consequences of global warming [2,3,4]
Climate change is intended to shift forest dynamics in the following decades [1]
The forest productivity predictions derived from these climatic projections revealed a decreasing trend in mean S under climate change

Summary

Introduction

Declines in forest productivity and fast changes in species suitability are among the potential negative consequences of global warming [2,3,4]. These consequences may compromise the ability of forest ecosystems for producing goods and services, leading to socioeconomic fallouts, such as scarcity in timber supply chains [5], turns in timberland value appreciation [6], and food and energy shortages in rural vulnerable communities [7]. The concern for proactively adapting to shifts in forest productivity has provoked a scientific turnaround in the field of empirical growth and yield modelling [1,8]. A variety of supervised learning techniques have been used for this purpose [10,11,12], yielding, overall, successful results (R2 ∼ 0.3–0.7)

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