Deforestation and greenhouse gas emissions could arise when replacing palm oil with other vegetable oils.

Oil crops are among the main drivers of global land use changes. Palm oil is possibly the most criticized, as a driver of primary tropical forests loss. This has generated two different reactions in its use in various sectors (e.g., food, feed, biodiesel, surfactant applications, etc.): from one side there is a growing claim for deforestation-free palm oil, whereas on the other side the attention raised towards other vegetable oils as possible substitutes, such as soybean, rapeseed and sunflower oil. We assess potential land use changes and consequent greenhouse gas (GHG) emissions for switching from palm oil to other oils and compare this solution to deforestation-free palm oils. We consider three scenarios of 25 %, 50 % and 100 % palm oil replacement in the eight major oil crop producing countries. Total GHG emissions account for anthropogenic emissions generated along the life cycle of the field production process and potential forest carbon stock losses from land use change for oil crops expansion. Replacing palm oil with other oils would have a worthless effect in terms of global emissions reduction since GHG emissions remain approximatively stable across the three scenarios, whereas it would produce a deforestation increase of 28.2 to 51.9 Mha worldwide (or 7 to 21.5 Mha if excluding the unlikely deforestation in USA, Russia, Ukraine and the offset deforestation in China, India). Conversely, if the global palm oil production becomes deforestation-free, its GHG emissions would be reduced by 92 %, switching from the current 371 to 29 Mt CO2eq per year. Although highlighting the historical unsustainability of oil palm plantations, results show that replacing them with other oil crops almost never represents a more sustainable solution, thus potentially questioning sustainability claims of palm oil free products with respect to deforestation-free palm oil.

Extending large-scale forest inventories to assess urban forests.

Urban areas are continuously expanding today, extending their influence on an increasingly large proportion of woods and trees located in or nearby urban and urbanizing areas, the so-called urban forests. Although these forests have the potential for significantly improving the quality the urban environment and the well-being of the urban population, data to quantify the extent and characteristics of urban forests are still lacking or fragmentary on a large scale. In this regard, an expansion of the domain of multipurpose forest inventories like National Forest Inventories (NFIs) towards urban forests would be required. To this end, it would be convenient to exploit the same sampling scheme applied in NFIs to assess the basic features of urban forests. This paper considers approximately unbiased estimators of abundance and coverage of urban forests, together with estimators of the corresponding variances, which can be achieved from the first phase of most large-scale forest inventories. A simulation study is carried out in order to check the performance of the considered estimators under various situations involving the spatial distribution of the urban forests over the study area. An application is worked out on the data from the Italian NFI.

A Phenological Model for Olive (Olea europaea L. var europaea) Growing in Italy.

The calibration of a reliable phenological model for olive grown in areas characterized by great environmental heterogeneity, like Italy, where many varieties exist, is challenging and often suffers from a lack of observations, especially on budbreak. In this study, we used a database encompassing many phenological events from different olive varieties, years, and sites scattered all over Italy to identify the phases in which site-enlarged developmental rates can be well regressed against air temperature (Developmental Rate function, DR) by testing both linear and nonlinear functions. A K-fold cross-validation (KfCV) was carried out to evaluate the ability of DR functions to predict phenological development. The cross-validation showed that the phases ranging from budbreak (BBCH 01 and 07) to flowering (BBCH 61 and 65) and from the beginning of flowering (BBCH 51) to flowering can be simulated with high accuracy (r2 = 0.93-0.96; RMSE = 3.9-6.6 days) with no appreciable difference among linear and nonlinear functions. Thus, the resulting DRs represent a simple yet reliable tool for regional phenological simulations for these phases in Italy, paving the way for a reverse modeling approach aimed at reconstructing the budbreak dates. By contrast, and despite a large number of phases explored, no appreciable results were obtained on other phases, suggesting possible interplays of different drivers that need to be further investigated.