Use of multivariate approaches in biomass energy plantation harvesting: logistics advantages

Agricultural biomass supply chain consisting of multiple harvesting, storage, pre-processing, and transport operations.  This network operates in space and time coordinates and produces empirical data used for many purposes, including wood-flow planning, harvesting cost calculation and work rate setting.  The aim of this study was to explore and propose the use of a multivariate approach, namely, the Partial Least Squares (PLS) multivariate regression approach and compare its performance with the commonly used Ordinary Linear Regression (OLS).  In particular, the study aimed at comparing the main statistical significance of indicators attributed to models calculated with OLS and PLS regressions from the same original datasets, for the purpose of quantifying the eventual improvement, obtained with the new techniques.  The dataset is composed by a series of measurements (harvesting distance, load carried, plantation production, numbers of plants harvested, and tractor engine power) conducted in a harvesting yard of a poplar plantation, to forecast the demanded working times.  The technical analysis was accompanied by economic scenarios, based on three hypothetical harvesting yards.  The results indicated that the PLS innovative approach is better performing; model error indicators are 5%-6% lower than those estimated with the OLS method.  From an economic point of view the harvesting cost per ton ranges among 8.69-14.59 € t-1, 12.10-16.56 € t-1 and 13.18-16.31 € t-1 referring to the different load capacity of the trailers, using the PLS model.  Based on these results the differences between PLS and OLS varied up to 40 € ha-1.  PLS modeling and more in general the advanced multivariate approach, are getting increasingly popular, because they are very robust and are particularly suitable for modeling complex systems.   Keywords: harvesting, biomass, logistics, machine costs, multivariate statistics, ordinary linear regression, partial least square

Sustainability assessment of a self-consumption wood-energy chain on small scale for heat generation in central Italy

The sustainability of a small-scale self-consumption wood-energy chain for heat generation in central Italy was analyzed from a technical, economic and energetic point of view. A micro-chain was developed within the CRA-ING farm at Monterotondo (Rome, Italy): The purpose of this system was to produce biomass for supplying a heating plant within the CRA-ING property as a substitute for diesel fuel. A poplar short rotation coppice, established with clones AF2, AF6 and Monviso, fed the micro-chain. The rotation was biennial. The average plantation production (Mgd.m.·ha−1·year−1) was 10.2, with a maximum of 13.53 for the twin-rows AF2 and a minimum of 8.00 for the single-row Monviso. The economic assessment was based on the Net Present Value (NPV) method and the equivalent annuity cost, and found an average saving of 15.60 €·GJ−1 of heat generated by the wood chips heating system in comparison with the diesel heating system over a 10 year lifetime of the thermal power plant. The energy assessment of the poplar plantation, carried out using the Gross Energy Requirements method, reported an energy output/input ratio of 12.3. The energy output/input ratio of the whole micro-chain was 4.5

A Three-Step Neural Network Artificial Intelligence Modeling Approach for Time, Productivity and Costs Prediction: A Case Study in Italian Forestry

The improvement of harvesting methodologies plays an important role in the optimization of wood production in a context of sustainable forest management. Different harvesting methods can be applied according to forest site-specific condition and the appropriate mechanization level depends on a number of factors. Therefore, efficiency and functionality of wood harvesting operations depend on several factors. The aim of this study is to analyze how the different harvesting processes affect operational costs and labor productivity in typical small-scale Italian harvesting companies. A multiple linear regression model (MLR) and artificial neural network (ANN) have been carried out to predict gross time, productivity and costs estimation in a series of qualitative and quantitative variables. The results have created a correct statistical model able to accurately estimate the technical parameters (work time and productivity) and economic parameters (costs per unit of product and per hectare) useful to the forestry entrepreneur to predict the results of the work in advance, considering only the values detectable of some characteristic elements of the worksite

Transport Cost Estimation Model of the Agroforestry Biomass in a Small-Scale Energy Chain

The delivery of biomass products from the production place to the point of final transformation is of fundamental importance within the constitution of energy chains based on biomass use as a renewable energy source. Transport can be one of the most economically expensive operations of the entire biomass energy production process, which limits choices in this sector, often inhibiting any expansive trends. A geographic identification, through remote sensing and photo-interpretation, of the different biomass sources was used to estimate the potential available biomass for energy in a small-scale supply chain. This study reports on the sustainability of transport costs calculated for different types of biomass sources available close a biomass power plant of a small-scale energy supply chain, located in central Italy. To calculate the transport cost referred to the identified areas we used the maximum travel time parameter. The proposed analysis allows us to highlight and visualize on the map the areas of the territory characterized by greater economic sustainability in terms of lower transport costs of residual agroforestry biomass from the collection point to the final point identified with the biomass power plant. The higher transport cost was around €40 Mg−1, compared to the lowest of €12 Mg−1

Efficient Estimation of Biomass from Residual Agroforestry

Cost-effective sampling methods for the estimation of variables of interest that are time-consuming are a major concern. Ranked set sampling (RSS) is a sampling method that assumes that a set of sampling units drawn from the population can be ranked by other means without the actual measurement of the variable of interest. We used data on vegetation dynamics from satellite remote sensing as a means in which to rapidly rank sampling units across various land covers and to estimate their residual agroforestry biomass contribution for a small cogeneration facility located in the center of a study area in central Italy. A remote sensing map used as an auxiliary variable in RSS enabled us to cut down the photo-interpretation of the residual biomass present in sampling units from 745 to 139, increase the relative precision of the estimate over common simple random sampling, and avoid individual subjective bias being introduced. The photo-interpretation of the sampling units resulted in a 1.12 Mg ha−1 year−1 mean annual density of residual biomass supply, although unevenly distributed among land cover classes; this led to an estimate of a yearly supply of 132 Gg over the whole 2276 km2 wide study area. Further applications of this study might include the spatial quantification of biomass supply-related ecosystem services

Environmental Sustainability of Heat Produced by Poplar Short-Rotation Coppice (SRC) Woody Biomass

As demonstrated for some time, the reduction of greenhouse gases in the atmosphere can also take place using agroforestry biomass. Short-rotation coppice (SRC) is one of the sources of woody biomass production. In our work, the supply of woody biomass was considered by examining four different cutting shifts (2, 3, 4 and 5 years) and, for each, the Global Warming Potential (GWP) was evaluated according to the IPCC 2007 method. Regarding the rotation cycle, four biomass collection systems characterized by different levels of mechanization were analyzed and compared. In this study, it was assumed that the biomass produced by the SRC plantations was burned in a 350 kWt biomass power plant to heat a public building. The environmental impact generated by the production of 1 GJ of thermal energy was assessed for each of the forest plants examined, considering the entire life cycle, from the field phase to the energy production. The results were compared with those obtained to produce the same amount of thermal energy from a diesel boiler. Comparing the two systems analyzed, it was shown that the production and use of wood biomass to obtain thermal energy can lead to a reduction in the Global Warming Potential of over 70% compared to the use of fossil fuel

Multi-Parametric Approach to Management Zone Delineation in a Hazelnut Grove in Italy

The increase in high-density hazelnut (Corylus avellana) areas drives the interest in practices of precision management. This work addressed soil apparent electrical conductivity (ECa), RGB aerial (UAV) images, proximal sensing, and field scouting in delineating and validating management zones (MZs) in a 2.96 ha hazelnut grove in Italy. ECa data were fitted to a semi-variogram, interpolated (simple kriging), and clustered, resulting in two MZs that were subjected to soil analysis. RGB imagery was used to extract tree canopies from the soil background and determine two vegetation indices (VIs) of general crop status: the Visible Atmospherically Resistant Index (VARI) and the Normalized Green-Red Difference Index (NGRDI). Then, plant growth parameters were manually assessed (tree height, crown size, etc.) and a proximal VI, the Canopy Index (CI), was determined with the MECS-VINE® vertical multisensor. MZ1 was characterized by lower ECa values than MZ2. This was associated with a lower clay content (9% vs. 21% in MZ1 vs. MZ2) and organic matter content (1.03% vs. 1.51% in MZ1 vs. MZ2), indicating lower soil fertility in MZ1 vs. MZ2. Additionally, hazelnut trees had significantly smaller canopies (1.42 vs. 1.94 m2 tree−1) and slightly lower values of VARI, NGRDI, and CI in MZ1 vs. MZ2. In conclusion, our approach used ECa to identify homogeneous field areas, which showed differences in soil properties influencing tree growth. This is the premise for differential hazelnut management in view of better efficiency and sustainability in the use of crop inputs