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Abstract Auri (Acacia auriculiformis A. Cunn. ex Benth.) is one of pioneer tree species developed in forest and land rehabilitation. This species can be used as a short-rotation plantation forest for biomass energy source that produces small diameter stem. The potential use of small diameter auri needs to be supported by accurate biomass estimation. This study aims at developing biomass estimation model for young, small diameter auri tree and comparing the local model to generic model. Measurements were carried out on 92 samples of 2-years old auri tree planted with stand densities of 1850-2500 trees/ha. Data was analysed using 8 local models and compared to 5 generic models. Result of the study shows that the best model for estimating small diameter auri biomass is B1 model (B = 0.016(D 20)2.78). The comparison of local and generic models suggested that the local model is better in predicting the auri biomass. This model is valid for small diameter auri species in West Nusa Tenggara Province. This model also seems reliable to apply in similar climatic region, but need a local data validation.

Due to emerging high spectral resolution, remote sensing techniques and ongoing developments to retrieve the spectrally resolved vegetation fluorescence spectrum from several scales, the light reactions of photosynthesis are receiving a boost of attention for the monitoring of the Earth's carbon balance. Sensor-retrieved vegetation fluorescence (from leaf, tower, airborne or satellite scale) originating from the excited antenna chlorophyll a molecule has become a new quantitative biophysical vegetation parameter retrievable from space using global imaging techniques. However, to retrieve the actual quantum efficiencies, and hence a true photosynthetic status of the observed vegetation, all signal distortions must be accounted for, and a high-precision true vegetation reflectance must be resolved. ESA's upcoming Fluorescence Explorer aims to deliver such novel products thanks to technological and instrumental advances, and by sophisticated approaches that will enable a deeper understanding of the mechanics of energy transfer underlying the photosynthetic process in plant canopies and ecosystems.

Amazon forest management plans have a variety of effects on carbon emissions, both positive and negative. All of these effects need to be quantified to assess the role of this land use in climate change. Here, we contribute to this effort by evaluating the carbon stocks in logs and timber products from an area under forest management in the southeastern portion of Acre State, Brazil. One hundred and thirty-six trees of 12 species had DBH ranging from 50.9 cm to 149.9 cm. Basic wood density ranged from 0.3 cm−3 to 0.8 g cm−3 with an average of 0.6 g cm−3. The logs had a total volume of 925.2 m3, biomass of 564 Mg, and carbon stock of 484.2 MgC. The average volumetric yield coefficient (VYC) was 52.3% and the carbon yield coefficient (CYC) was 53.2% for logs of the 12 species. The sawn-wood products had a total volume of 484.2 m3, biomass of 302.6 Mg, and carbon stock of 149.9 MgC. Contributions of the different species to the total carbon stored in sawn-wood products ranged from 2.2% to 21.0%. Means and standard deviations for carbon transferred to sawn-wood products per-species from the 1252.8-ha harvested area ranged from 0.4 ± 1.1 MgC to 2.9 ± 0.4 MgC, with the largest percentages of the total carbon stored in wood products being from Dipteryx odorata (21.0%), Apuleia leiocarpa (18.7%), and Eschweilera grandiflora (11.7%). A total of 44,783 pieces of sawn lumber (such as rafters, planks, boards, battens, beams, and small beams) was obtained from logs derived from these trees. Lumber production was highest for boards (54.6% of volume, 47.4% of carbon) and lowest for small beams (1.9% of volume, 2.3% of carbon). The conversion factor for transforming log volume into carbon stored in sawn-wood products was 16.2%. Our results also show that species that retain low amounts of carbon should be allowed to remain in the forest, thereby avoiding low sawmill yield (and consequent generation of waste) and allowing these trees to continue fulfilling environmental functions.

In sub-Saharan Africa (SSA), diets are largely based on cereal or root staple crops. Together with socio-cultural change, economic and demographic growth could boost the demand for meat, with significant environmental repercussions. We model meat consumption pathways to 2050 for SSA based on several scenarios calibrated on historical demand drivers. To assess the consequent environmental impact, we adopt an environmentally-extended input-output (EEIO) framework and apply it on the EXIOBASE 3.3 hybrid tables. We find that, depending on the interplay of resources efficiency and demand growth, by 2050 the growth in meat consumption in SSA could cause a growth in greenhouse gases emissions of 1.4 [0.9–1.9] Gt CO2e/yr (~175% of current regional agriculture-related emissions), which is an extension of cropping and grazing-related land of 15 [12.5–21] · 106 km2 (one quarter of today’s global agricultural land), the consumption of an additional 36 [29–47] Gm3/yr of blue water (nearly doubling the current regional agricultural consumption), an eutrophication potential growth of 7.6 [4.9–9.5] t PO4e/yr, and the consumption of additional 0.9 [0.5–1.4] EJ/yr of fossil fuels and 49 [32–73] TWh/yr of electricity. These results suggest that—in the absence of significant improvements in the regional sectoral resource efficiency—meat demand growth in SSA is bound to become a major global sustainability challenge. In addition, we show that a partial substitution of the protein intake from the expected growth in meat consumption with plant-based alternatives carries additional significant potential for mitigating environmental impacts. The policies affecting both farming practices and dietary choices will thus have a significant impact on the SSA and global environmental flows.

As the world population is increasing rapidly, food and water demands are the most crucial problem for humanity. In some areas of the world, water or environment is unsuitable for plant growth; hydroponic systems can provide a suitable environment for crop production with effective management of natural resources. Internet of Things paradigm based automated systems has been creating an excellent opportunity for monitoring and controlling agriculture by minimizing the cost and maximizing the profit significantly over the past decade. The reduction of the cost can be achieved by sufficient usage of resources and setting up optimum operational parameters for agricultural devices. This paper presents an optimization scheme with novel objective function for hydroponics environment parameters management with efficient energy consumption. The proposed approach provides optimal energy and resource utilization in the hydroponics system with setting up a working level and operational duration to the actuators. We have developed an optimization scheme with objective function for optimal humidity and water level control based on fuzzy logic, which can support the optimal measurement for crop growth with energy efficiency. Fuzzy logic control is applied for the compromise between actuators working level and operational duration. A real hydroponics environment has been implemented and presented to evaluate the effectiveness of the proposed approach. It can be assessed through the simulation results that the optimization module achieves a signification reduction (18%) in energy consumption as compared to the other scheme.

Abstract Growing non-food crops in marginal lands has been proposed as a solution to avoid land competition with food production. Mapping marginal agricultural lands is therefore fundamental for the sustainable development of rural landscapes. This study proposes a method based on remote sensing data to identify marginal agricultural lands for the production of wood biomass, and analyse potential trade-offs and synergies between the new wood crops, food production, and Ecosystem Services (ES) provided by vegetation. The province of Rovigo (northern Italy) was chosen as a representative case study. Three classes of marginal agricultural lands were mapped through the use of the Soil Adjusted Vegetation Index (SAVI): i) abandoned or unused agricultural lands, ii) potentially poorly or non-managed croplands, and iii) potentially low productivity croplands. Results showed that marginal agricultural lands cover 1.7% of the agricultural areas of the province, and approximately 13,642 MWh yr−1 of Second-Generation (2G) bioenergy can be produced in marginal agricultural areas while enhancing ES provided by vegetation, and avoiding any trade-off with food production. Since this energy potential covers just 8.4% of the total potential authorized in the province, the enhancement of ES could provide a suitable argument to support the conversion of marginal agricultural lands and increase the multifunctionality of the agricultural landscape.

The standard deviation of the Horizontal Wind Speed as a proxy of wind turbulence is used to compare the apparent wind turbulence measured by an off-shore floating Doppler lidar to the one measured by a fixed lidar on a metmast. We use statistical analysis based on clustering the horizontal wind speed measured by the floating lidar as well as buoy angular amplitude and period under the approximation of harmonic motion. Three scenarios with different wave and wind conditions are discussed from the IJmuiden's test campaign (North Sea.). This work was supported via Spanish Government–European Regional Development Funds project PGC2018-094132-B-I00 and EU H2020 ACTRIS-2 (GA 654109). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a María-de-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigacion (Spanish National Science Foundation). Peer Reviewed

This report provides a complete methodological description and background information of the Dutch National System for Greenhouse gas reporting of the LULUCF sector. It provides detailed description of the methodologies, activity data and emission factors that were used. Each of the reporting categories Forest Land, Cropland, Grassland, Wetlands, Settlements, Other land and Harvested Wood Products are described in a separate chapter. Additionally it gives a table-by-table elaboration of the choices and motivations for filling the CRF tables for KP-LULUCF. Dit rapport geeft de methodologische achtergrondinformatie die gebruikt wordt binnen het nationale systeem om de broeikasgasemissies voor de LULUCF (landgebruik en bosbouw) sector te berekenen zoals die aan de VN Klimaatconventie (UNFCCC) en het Kyoto Protocol (KP) worden gerapporteerd. Het rapport geeft gedetailleerde beschrijvingen van de gehanteerde methodologie, gebruikte activiteitendata en emissiefactoren. De te rapporteren categorieën Bos (Forest land), Bouwland (Cropland), Grasland (Grassland), Wetlands, Bebouwd gebied (Settlements), Ander land, en geoogste houtproducten worden per hoofdstuk beschreven. Daarnaast worden in een apart hoofdstuk de gebruikte aggregatiestappen gegeven om tot de berekeningen voor het Kyoto Protocol te komen en worden voor iedere KP-LULUCF-CRF-tabel de gemaakte keuzes om de tabel te vullen, beschreven en gemotiveerd.