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The global production of vegetable oils exceeds 200 million tonnes per year, with almost 40% for food use, and around 330 M hectares occupied by oil crops. The most produced is palm (40% if palm kernel oil is included), followed by soybean oil (28%), rapeseed oil (12%) and sunflower oil (9%). Some of these oil crops, particularly oil palm plantations and soy cultivations, are among the main drivers of global land use changes (LUC) and deforestation. In particular palm oil has been one of the most highly criticized due to the link between oil palm cultivation expansion and the loss of primary tropical forests, observed in recent decades. This issue has generated two different responses in the food sector: some players decided to produce and/or use deforestation-free palm oil. Other actors chosen to replace palm oil with other vegetable oils, such as soybean, rapeseed and sunflower oil.Considering the importance of a proper land management in view of the food-ecosystems-resources nexus, this study assesses the potential LUC and the related GHG emissions that can occur by using sustainable palm oil or replacing it with the other oils for food use. A methodology was developed to assess the potential GHG emissions from the LUC due to alternative oil crops expansion at detrimental of high carbon content areas, such as forests or perennial croplands, and the GHG emissions from the production process though a Life Cycle Assessment (LCA).Under the scenario of 100% replacement of palm and palm kernel oil globally, the extra-land needed to produce the additional alternative oils was determined in their three top producer countries using yield data from literature. An expansion algorithm considering suitability and distance from roads and existing oil crops was developed to determine the potential LUC which may occur in the selected countries. The potential GHG emissions from deforestation and other LUC were calculated from the carbon stock data of the FAO Forest Resource Assessment and IPCC; the field production of the four oils was reconstructed to calculate anthropogenic GHG emissions using relevant LCA existing databases. Results show that deforestation-free palm oil is the less impacting in terms of GHG emissions per oil ton thanks to its far highest oil yield. Replacing sustainable palm oil with any other alternative oil is never a favourable solution (Fig. 1), entailing a potential GHG emissions increase from 0.94-0.96 Mg CO2 per ton of palm oil replaced by sunflower oil produced in Ukraine or in Russia (where deforestation is unlikely), to 4.38 Mg CO2 per ton of palm oil replaced by soybean oil produced in Brazil, up to 13.65 Mg CO2 per ton of palm oil replaced by soybean oil produced in Argentina. Figure 1. GHG emissions in Mg CO2eq t-1 from LCA (blue bars) and LUC (green bars) with 100% palm oil replacement. Based on national trends and forest policies, potential deforestation can be likely (full green), likely with limitation (dense dots), likely with offset (oblique lines), unlikely (scattered dots). Vertical lines for palm oil include deforestation.

Cover crops are cultivated during unproductive periods to provide ecosystem services, such as preventing nitrogen (N) leaching, and have the potential to increase soil organic carbon (C), thus contributing to climate change mitigation. Their effect on soil C storage depends on C input, which is challenging to quantify due to belowground sampling (roots and C deposited in the soil). Leguminous cover crops (in pure stands or in mixtures) increase the productivity of following main crops due to high N input, but also the risk of N losses, compromising the mitigation effect. As C and N services vary with cover crop type, tradeoffs could be minimized by species selection. The aim of this study was to assess C input from different cover crop species and mixtures, as well as N uptake in their biomass, and their effect on soil mineral N (SMN) and yield of the following crop.We conducted a field experiment in 2020-2021 at Foulum, Denmark (temperate oceanic climate, sandy loam soil), with seven treatments (five cover crops, control with volunteers, and bare soil) replicated four times. Cover crops undersown in spring barley (Hordeum vulgare L.) in May 2020 were: Lolium perenne L. (ryegrass, RG), Trifolium pratense L. (red clover, RC), Plantago lanceolata L. (plantain, PL) and the mixtures RG-PL and RG-PL-RC. On August 24, 2020, after barley harvest, PVC cylinders (diameter: 29.5 cm, height: 30 cm) were inserted in each plot (25 cm depth) and used for 13C-CO2 multiple-pulse isotopic labeling. Two sessions per week were conducted until cover crop sampling in November 2020, when C and N in above- and belowground biomass, as well C deposited in the soil were determined. Monthly soil sampling (20 cm depth) was performed from August 2020 until April 2021 to assess SMN. Then barley was sown to evaluate cover crops residual effect.Aboveground biomass was lowest in RG (1.5 Mg ha-1), and highest in RG-PL-RC (5.4 Mg ha-1). Total C input (above- and belowground) ranged from 1.6 to 4.3 Mg ha-1, with RG-PL-RC (highest) being significantly higher than RG-PL and RG (lowest). The same pattern applied to total N input, ranging from 74 to 202 kg ha-1. All cover crop treatments had lower SMN than bare soil and volunteers in August 2020. SMN increased from August until April 2021 with all cover crops except RG, and decreased with bare soil and volunteers. SMN in April was 15 kg ha-1 higher with RC and RG-PL-RC than RG. Barley yield following leguminous cover crops was comparable to plots fertilized with 100 kg ha-1 of mineral N, while non-legumes to 40 kg ha-1. Overall, the mixture with RC provided the greatest C input and positive residual effect. The small change in SMN in April indicate that biomass N was converted into mineral N and taken up by barley during the growing season, thus not increasing the risk of N losses.

The sustainable policies and practices such as agricultural, forestry and land management are crucial for ensuring the long-term provision of ecosystem services. According to the European Green Deal strategy, at least 3 billion more trees should be planted in the European Union by 2030. In support of this target, the Italian National Forest Strategy proposes actions aimed at reforestation, afforestation and agroforestry. Enhancing forest density and cover enhances the capacity to remove CO2 and store it as organic carbon in the global terrestrial biosphere. Spatial decision support tools can support policy makers in addressing these challenges and relevant assessment of land-based mitigation options to reduce emissions in the land sector. Despite the specific goal set in the European Green Deal strategy, there is still no clear understanding of which territory in Italy will be involved in the planned actions. Therefore, there is a need to build suitability maps to determine the future change of the land cover in connection with the European strategies and planned actions up to 2030. This study suggests not only possible future land cover maps for the Lazio region, compiled in accordance with the land use policy, but also an assessment of the planned carbon sequestration based on the proposed maps.

Olive (Olea europaea L.) trees are traditionally cultivated in the Mediterranean basin, providing both healthy food and ecosystem services, such as climate change mitigation and soil erosion control, particularly in arid areas. Despite its importance, olive phenology, as impacted by climate change, is under-studied. To tackle this gap, we assessed the potential of feed-forward artificial neural network models to predict five main olive phenophases (apex budbreak, inflorescence, flowering, pit hardening and olive maturation index 1) at their onset for cultivars ‘Picholine’, ‘Carolea’ and ‘Coratina'. The dataset was collected from seven sites across Italy during the years 1997-2000. Due to gaps in the dataset, the models were initialized by supervised training with the subset of full phenological observations, followed by semisupervised training based on the full dataset and iterative estimations of the missing observations. The softmax activation function was used in the output layer by interpreting the incremental phenological transitions as proportional to probabilities. The networks with at least four hidden layers activated by the sigmoid function and trained with the momentum method and linearly-decreasing parameters were best performing (validation RMSE of 15.5 d and 17.1 d for ‘Picholine’ and ‘Carolea’, respectively). Daily insolation consistently improved budbreak prediction with respect to daily mean temperature, suggesting the operation of photoreceptor activation mechanisms. Inflorescence was better predicted when daily minimum temperature was added, consistent with a chilling-warm requirement mechanism. Flowering was less consistent, but mean temperature was a primary controlling cue. Therefore, each phenophase is likely controlled by different climate cues. When tested on two independent flowering dates in 2017 and 2018 from one of the sites , the best performing models for each cultivar gave median errors of 4.3 d, 12.1 d, 7.4 d and 3.7 d for the ‘Picholine’, ‘Carolea’, ‘Coratina’ and the combinaed ‘Picholine+Carolea+Coratina’, respectively. The worse predictions for 'Carolea' is likely due to the hypothesized sensitivity of this cultivar to climate change, that occurred in the years between the training and the testing observations. Therefore, the olive sensitivity to climate change could be strongly cultivar-dependent, which calls for more in-depth investigation in the future. The calibrated models can be used both as operational and hypothesis-testing tools to study climate change effects on olive phenology.