The Importance of Extraction Protocol on the Analysis of Novel Waste Sources of Lignocellulosic Biomass

As the utilization and consumption of lignocellulosic biomass increases, so too will the need for an adequate supply of feedstock. To meet these needs, novel waste feedstock materials will need to be utilized. Exploitation of these novel feedstocks will require information both on the effects of solvent extraction on the succeeding analysis of potential novel feedstocks and how accurate current methodologies are in determining the composition of novel lignocellulosic feedstocks, particularly the carbohydrate and lignin fractions. In this study, the effects of solvent extraction on novel feedstocks, including tree foliage, tree bark and spent mushroom compost, with 95% ethanol, water and both sequentially were examined. Chemical analyses were carried out to determine the moisture content, ash, extractives, post-hydrolysis sugars, Klason lignin (KL) and acid-soluble lignin (ASL) within the selected feedstocks. The result of extraction could be seen most strongly for Klason lignin, with a strong association between higher levels of Klason lignin levels and greater amounts of non-removed extractives (tree foliage and bark). Higher Klason lignin levels are reported to be due the condensation of non-removed extractives during hydrolysis, hence the lower Klason lignin determinations following extraction are more exact. In addition, total sugar determinations were lower following extractions. This is because of the solubility of non-cell-wall carbohydrates; thus, the determinations following extraction are more accurate representations of structural cell-wall polysaccharides such as cellulose. Such determinations will assist in determining the best way to utilize novel feedstocks such as those analyzed in this work.

Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts

Tropical forest disturbances linked to fire usage cause large amounts of greenhouse gas (GHG) emissions and environmental damages. Supporting precise GHG estimations and counteracting illegal fire usages in the tropics require timely and thematically detailed large-scale information on fire-related forest disturbances. Multi-sensor optical and radar detection and ranging (radar) remote sensing data combined with active fire alerts shows the potential for a more in-depth characterization of fire-related forest disturbances. We utilized dense optical (Landsat-7, Landsat-8 and Sentinel-2) and radar (Sentinel-1) time series to individually map forest disturbances in the province of Riau (Indonesia) for 2018–2019. We combined the sensor-specific optical and radar forest disturbance maps with daily active fire alerts and classified their temporal relationship (predating, coinciding, postdating) into seven so-called archetypes of fire-related forest disturbances. The archetypes reflect sensor-specific sensitives of optical (e.g., changes in tree foliage) and radar (e.g., changes in tree structure) data to detect varying types of forest disturbances, ranging from either a loss of tree foliage and/or structure predating, coinciding or postdating fires. These can be related to different magnitudes of fire-related forest disturbances and burn severities and can be associated with specific land management practices, such as slash-and-burn agriculture and salvage logging. This can support policy development, local and regional forest management and law enforcement to reduce illegal fire usage in the tropics. Results suggest that a delayed or opposing forest disturbance detection in the optical and radar signal is not only caused by environmental influences or different observation densities but, in some cases, such as fire-related forest disturbances, can be related to their different sensitives to detect changes in tree foliage and structure. Multi-sensor-based forest monitoring approaches should, therefore, not simply combine optical and radar time series on a data level, as it bears the risk of introducing artefacts.

Fractional order modeling and recognition of nitrogen content level of rubber tree foliage

The Nondestructive estimation method of nitrogen content level of rubber tree foliage was investigated utilizing nearinfrared (NIR) spectroscopy and Grünwald-Letnikov fractional calculus. Four models, including partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), extreme learning machine (ELM) and convolutional neural networks (CNN) are applied to construct the nitrogen estimation model. The results show that models established by 0.6-order or 1.6-order spectra achieved better performance than models with integer-order spectra. Afterward, the successive projections algorithm (SPA) is applied to reduce the number of variables, which is critical for developing portable nitrogen-level detector devices for rubber trees. The PLS-DA method achieved the best performance with an optimal recognition rate (97.73%) using the 1.6-order spectra. The results suggest that nitrogen content of rubber trees could be reliably estimated by fractional calculus processed NIR spectra. The method proposed here has a wide range of applicability and can provide more useful information for NIR spectral analysis in agriculture as well as other fields.

Postharvest Population Reservoirs of Coffee Berry Borer (Coleoptera: Curculionidae) on Hawai’i Island

Coffee berry borer, Hypothenemus hampei Ferrari (Coleoptera: Curculionidae: Scolytinae), is the most damaging insect pest of coffee worldwide. Old coffee berries (raisins) are widely acknowledged as coffee berry borer reservoirs, yet few studies have attempted to quantify coffee berry borer populations in raisins remaining on farms postharvest. We collected ground and tree raisins at six coffee farms on Hawai’i Island to assess raisin density, infestation, coffee berry borer abundance, and adult mortality in three areas of each farm: trees, driplines (ground below the tree foliage), and center aisles (ground between tree rows). We also assessed infestation of the new season’s crop by conducting whole-tree counts of infested green berries. Mean raisin density was significantly higher in the dripline compared to the center aisle and trees (131 vs 17 raisins per m2 and 12 raisins per tree, respectively). Raisin infestation was significantly higher in samples from trees (70%) relative to those from the dripline (22%) and center aisle (18%). Tree raisins had significantly higher coffee berry borer abundance compared to both areas of the ground (20 vs 3–5 coffee berry borer per raisin). Adult mortality was significantly higher on the ground (63–71%) compared to the trees (12%). We also observed a significant positive correlation between ground raisin density and infestation of the new season’s crop. Across all farms, we estimated that 49.5% of the total coffee berry borer load was present in dripline raisins, 47.3% in tree raisins, and 3.2% in center aisle raisins. Our findings confirm the importance of whole-farm sanitation in coffee berry borer management by demonstrating the negative impact that poor postharvest control can have on the following season’s crop.