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Alley-cropping agroforestry enhances carbon sequestration compared to open cropland and open grassland, and improves soil biological habitat and erosion resistance relative to open cropland, based on measured ecosystem functions at five sites in Germany.

IntroductionYield and its stability are both vital characteristics to evaluate the viability of cropping systems. However, the current frames of field research hardly allow an accurate evaluation of short-term effect stability. Therefore, over nine German environments (three years and three locations), first crop, maize, total harvested dry biomass yield (DMY), and maize dry matter content (DMC) variability were evaluated through a risk assessment in an organically managed silage maize experiment comprising 18 cropping systems.Material and methodsThe treatment factors included first crop group (pure legume, legume–cereal mixture), first crop (winter pea, hairy vetch and their mixtures with rye, control), management—incorporating first crop use and tillage (double cropping system no-till, double cropping system reduced till, double-cropped, mulched system terminated with roller-crimper, control), fertilization and mechanical weed control (yes–no), and row width (75 cm, 50 cm).Results and discussionThe first crop DMY and maize DMC had a positive relationship with its variance, whereas maize DMY and total DMY had a negative relationship. The differences in risks were governed by system (number of crops), management and first crop (group), and followed compatible patterns with what was observed for their influence on the mean of the parameters. The pedological and climatic conditions, especially near maize sowing and establishment, and therefore the length of season are crucial in double cropping and double-cropped, mulched systems. In its current state, the location in the northern region of Germany was not well suited for the studied alternative systems, whereas in the other regions (central and south), double cropping systems with reduced tillage as well as double-cropped systems with pure legume mulches may offer alternative management systems for silage maize. Further optimization of the critical sowing and establishment phase may result in more diversified options for double cropping and double-cropped, mulched systems in the future.

Biochar is an emerging biomaterial for managing residual biomass while simultaneously sequestering carbon. To extend the biochar value chain, applying biochar to enhance anaerobic digestion (AD) processes is gaining attention in the context of a circular economy and cascading use of biomass. However, the comparative effects of various biochar dosages under normal and severe AD conditions are still unclear. To further our understanding of its potential application, this work investigated the impact of adding various biochar dosages on AD processes under normal and high substrate loadings. Three inoculum-to-substrate ratios (ISRs): one representing normal substrate loading (ISR 2) and two representing substrate overloading (ISR 1 and 0.5) were investigated. Each substrate loading rate was tested with a biochar dosage of 0% (control), 10%, and 25% based on substrate volatile solids. The results revealed that under the severe condition of high substrate overload (ISR 0.5), a high biochar dosage of 25% significantly increased cumulative methane production by 5.6% (p = 0.06) when compared to the control. Under the same condition (ISR 0.5, 25%), the time required to achieve a particular extent of ultimate methane potential was significantly reduced (p = 0.04), indicating that the methane production rate was increased. At ISR 0.5, the increase of process stability was also significant with 25% biochar addition, while the control (0%) and 10% biochar addition exhibited high variance among replicates. However, biochar did not affect AD processes under normal substrate loading (ISR 2) and mild substrate overload (ISR 1). Thus, a positive effect of biochar on the AD process was only observed under severe conditions with the highest biochar dosage. Future works should consider optimising substrate loadings and biochar dosages under real conditions when testing the practical application of biochar addition in AD processes.

Interest in biochar as an additive to enhance anaerobic digestion (AD) has grown in the context of biomass cascading use and the 2050 net-zero goal. However, few studies have investigated the effects of biochar on AD from a biochar production perspective, including biomass feedstocks and pyrolysis temperatures. To valorise biomass and better understand the mechanisms and environmental implications of using biochar in AD, this study investigated the effects of distinct biochar types on AD under acid stress-induced process inhibition using batch tests. The results demonstrated that biochar can mitigate acid stress and enhance the methane production rate. The kinetic rate constant of methane production is positively related to the buffer capacity of the tested biochars ( R ^2 = 0.88). The choice of feedstocks is a crucial factor ( P = 0.003), particularly the best-performing biochars derived from raw grass silage. In contrast, the pyrolysis temperature effect was less significant ( P = 0.18). Furthermore, the analysis of biochar indicates that the alkali (K) and alkaline earth (Ca, Mg) metals contained in biochar may be one of the important factors contributing to buffer capacity ( R ^2 = 0.82 to 0.86). Hence, buffer capacity is a crucial quality criteria when evaluating biochar for AD applications. Raw grass silage biochars are promising for acid stress mitigation due to their high buffer capacity, while carbon-rich woody biochars have high CO _2 sequestration potential. A compromise between mitigating acid stress and sequestering carbon is the use of pre-treated grass biochar. Overall, the use of biochar-enriched digestate offers a potential way to close material loops and complete the biomass-to-biochar value chain.

Abstract Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe.

Abstract Remote sensing data fusion is a powerful tool to gain information of quantitative and qualitative vegetation properties on field level. The aim of this study was to develop prediction models from sensor data fusion for fresh and dry matter yield (FMY/DMY) in extensively managed grasslands with variable degree of invasion by Lupinus polyphyllus. Therefore, a terrestrial 3d laser scanner (TLS) and a drone‐based hyperspectral camera was used to collect high resolution 3d point clouds and hyperspectral aerial orthomosaics of four extremely heterogenous grasslands. From 3d point clouds multiple features (vegetation height, sum of voxel, point density and surface structure) were extracted and combined with hyperspectral data to develop an optimized biomass model from random forest regression algorithm to predict FMY and DMY (ntrain = 130, ntest = 33). Models from hyperspectral data solitarily had the lowest prediction performance (FMY: R2 = 0.61, nRMSEr = 17.14; DMY: R2 = 0.59, nRMSEr = 19.37). Higher performance was gained by models derived from 3d laser data (FMY: R2 = 0. 76, nRMSEr = 13.3; DMY: R2 = 0. 74, nRMSEr = 15.1). A fusion of both sensor systems increased the FMY prediction performance up to R2 = 0.8; nRMSEr = 12.02 and the DMY prediction performance to R2 = 0.81 and nRMSEr = 12.06. The fusion of complementary sensor systems can increase the power to predict biomass yields of heterogenous and extensively managed grasslands. It is a novel alternative to labour‐intensive, traditional biomass prediction methods and to remote sensing methods using only single sensor data.

There are often negative side-effects associated with the traditional (silage) maize cropping system related to the unprotected soil surface. Reducing soil disturbance could enhance system sustainability. Yet, increased weed pressure and decreased nitrogen availability, particularly in organic agriculture, may limit the implementation of alternative management methods. Therefore, a field experiment was conducted at two distinct locations to evaluate the weed control efficiency of 18 organically managed silage maize cropping systems. Examined parameters were relative weed groundcover (GCweed) and its correlation with maize dry matter yield (DMY), relative proportion of dominant weed species (DWS) and their groups by life form (DWSgroup). Treatment factors comprised first crop (FC—winter pea, hairy vetch, and their mixtures with rye, control (sole silage maize cropping system—SCS)), management—incorporating FC use and tillage (double cropping system no-till (DCS NT), double cropping system reduced till (DCS RT), double cropped, mulched system (DCMS Roll) and SCS control), fertilization, mechanical weed control and row width (75 cm and 50 cm). The variation among environments was high, but similar patterns occurred across locations: Generally low GCweed occurred (below 28%) and, therefore, typically no correlation to maize DMY was observed. The number of crops (system), system:management and occasionally management:FC (group) influenced GCweed and DWS(group). Row width had inconsistent and/or marginal effects. Results suggest differences related to the successful inclusion of DCS and DCMS into the rotation, and to the altered soil conditions, additional physical destruction by shallow tillage operations, especially in the early season, which possibly acts through soil thermal and chemical properties, as well as light conditions. DCS RT could successfully reduce GCweed below 5%, whereas DCS NT and particularly DCMS (Mix) suffered from inadequate FC management. Improvements in DCMS may comprise the use of earlier maturing legumes, especially hairy vetch varieties, further reduction/omission of the cereal companion in the mixture and/or more destructive termination of the FC.

Traditional (silage) maize production often has negative side-effects related to unprotected soil surface. There are several possibilities to enhance system sustainability through reducing soil disturbance. However, implementation may be hindered due to reduced nitrogen availability and increased weed infestation, especially in organic agriculture. A field experiment to evaluate yield potential of 18 silage maize cropping systems under organic management was conducted at three distinct locations. Examined parameters were first crop, maize and total harvested dry matter yield (DMY), and maize dry matter content (DMC). Treatment factors included first crop (FC—winter pea, hairy vetch, and their mixtures with rye, control (SCS), management—incorporating FC use and tillage (double cropping system no-till (DCS NT), double cropping system reduced till (DCS RT), double cropped, mulched system terminated with roller-crimper (DCMS Roll), SCS control), fertilization, mechanical weed control—and row width (75 cm, 50 cm). A high variation among environments occurred, but similar patterns manifested across locations: Number of crops in the rotation had a high influence, followed by management and FC. Row width had only marginal and inconsistent effect. FC mixtures generally yielded higher than pure legumes. Maize DMY in DCS, DCMS was lower than or comparable to SCS. Maize DMC were environment-specifically below acceptable range, especially under DCMS. Total harvested DMY in DCS were similar to or greater than SCS. Results suggest differences from the optimization of farming operations for one (SCS) or two crops (DCS, DCMS) with strong effects at early maize development and on the length of season. FC use and tillage factors possibly altered the soil water, temperature, and mineralization dynamics, resulting in modified maize growth. DCS RT and DCMS Pure performed with the best maize yields, improved soil protection, and tillage reduction in the silage maize part of the rotation under organic management. However, alternative management systems, especially under DCS NT and DCMS (Mix) with studied maize maturity classes are less suited, particularly in cool and wet spring conditions, because of a potentially slower development of FC, a later establishment of maize plants and therefore, a shorter growing season for the maize crop.

European farmers and especially organic farmers rely on legume–grass mixtures in their crop rotation as an organic nitrogen (N) source, as legumes can fix atmospheric N, which is the most important element for plant growth. Furthermore, legume–grass serves as valuable fodder for livestock and biogas plants. Therefore, information about aboveground biomass and N fixation (NFix) is crucial for efficient farm management decisions on the field level. Remote sensing, as a non-destructive and fast technique, provides different methods to quantify plant trait parameters. In our study, high-density point clouds, derived from terrestrial laser scanning (TLS), in combination with unmanned aerial vehicle-based multispectral (MS) data, were collected to receive information about three plant trait parameters (fresh and dry matter, nitrogen fixation) in two legume–grass mixtures. Several crop surface height metrics based on TLS and vegetation indices based on the four MS bands (green, red, red edge, and near-infrared) were calculated. Furthermore, eight texture features based on mean crop surface height and the four MS bands were generated to measure horizontal spatial heterogeneity. The aim of this multi-temporal study over two vegetation periods was to create estimation models based on biomass and N fixation for two legume–grass mixtures by sensor fusion, a combination of both sensors. To represent conditions in practical farming, e.g., the varying proportion of legumes, the experiment included pure stands of legume and grass of the mixtures. Sensor fusion of TLS and MS data was found to provide better estimates of biomass and NFix than separate data analysis. The study shows the important role of texture based on MS and point cloud data, which contributed greatly to the estimation model generation. The applied approach offers an interesting method for improvements in precision agriculture.

Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of grassland with high spatial, temporal and radiometric resolution and coverage is available. The present study aimed at developing a robust model capable of estimating grassland biomass across a gradient of usage intensity throughout one growing season. Therefore, UAV-borne hyperspectral data from eight grassland sites in North Hesse, Germany, originating from different harvests, were utilized for the modeling of fresh matter yield (FMY) and DMY. Four machine learning (ML) algorithms were compared for their modeling performance. Among them, the rule-based ML method Cubist regression (CBR) performed best, delivering high prediction accuracies for both FMY (nRMSEp 7.6%, Rp2 0.87) and DMY (nRMSEp 12.9%, Rp2 0.75). The model showed a high robustness across sites and harvest dates. The best models were employed to produce maps for FMY and DMY, enabling the detailed analysis of spatial patterns. Although the complexity of the approach still restricts its practical application in agricultural management, the current study proved that biomass of grassland sites being subject to different management intensities can be modeled from UAV-borne hyperspectral data at high spatial resolution with high prediction accuracies.

Semi-natural grasslands contribute highly to biodiversity and other ecosystem services, but they are at risk by the spread of invasive plant species, which alter their habitat structure. Large area grassland monitoring can be a powerful tool to manage invaded ecosystems. Therefore, WorldView-3 multispectral sensor data was utilized to train multiple machine learning algorithms in an automatic machine learning workflow called ‘H2O AutoML’ to detect L. polyphyllus in a nature protection grassland ecosystem. Different degree of L. polyphyllus cover was collected on 3 × 3 m2 reference plots, and multispectral bands, indices, and texture features were used in a feature selection process to identify the most promising classification model and machine learning algorithm based on mean per class error, log loss, and AUC metrics. The best performance was achieved with a binary classification of lupin-free vs. fully invaded 3 × 3 m2 plot classification with a set of 7 features out of 763. The findings reveal that L. polyphyllus detection from WorldView-3 sensor data is limited to large dominant spots and not recommendable for lower plant coverage, especially single plant detection. Further research is needed to clarify if different phenological stages of L. polyphyllus as well as time series increase classification performance.

Organic farmers, who rely on legumes as an external nitrogen (N) source, need a fast and easy on-the-go measurement technique to determine harvestable biomass and the amount of fixed N (NFix) for numerous farm management decisions. Especially clover- and lucerne-grass mixtures play an important role in the organic crop rotation under temperate European climate conditions. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) are new promising tools for a non-destructive assessment of crop and grassland traits on large and remote areas. One disadvantage of multispectral information and derived vegetations indices is, that both ignore spatial relationships of pixels to each other in the image. This gap can be filled by texture features from a grey level co-occurrence matrix. The aim of this multi-temporal field study was to provide aboveground biomass and NFix estimation models for two legume-grass mixtures through a whole vegetation period based on UAV multispectral information. The prediction models covered different proportions of legumes (0-100% legumes) to represent the variable conditions in practical farming. Furthermore, the study compared prediction models with and without the inclusion of texture features. As multispectral data usually suffers from multicollinearity, two machine learning algorithms, Partial Least Square and Random Forest (RF) regression, were used. The results showed, that biomass prediction accuracy for the whole dataset as well as for crop-specific models were substantially improved by the inclusion of texture features. The best model was generated for the whole dataset by RF with an rRMSE of 10%. For NFix prediction accuracy of the best model was based on RF including texture (rRMSEP = 18%), which was not consistent with crop specific models.

Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.

With the central aim to reduce greenhouse gas emissions, agroforestry systems have become popular because they can provide biomass for bioenergy conversion and thus help replace fossil energy. This article compares the net energy balances of three biomass conversion techniques for an agroforestry system consisting of willows and two types of grassland biomass as well as separate stands of grassland and willow. The period of investigation was the second willow rotation (4–6 years after establishment). The biomass conversion techniques included combustion of hay (CH), integrated generation of solid fuel and biogas from biomass (IFBB), and anaerobic whole crop digestion (WCD). Compared to the first rotation (years 1–3), the net energy yield of the willow stands significantly increased. Nevertheless, the separate stand of willow had higher net energy yield than the agroforestry system. The IFBB technique led to an improvement in solid fuel quality through demineralization. CH and IFBB provided higher net energy yields than WCD.

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

Agroforestry systems (AFSs) are promoted as environmentally friendly and climate-change-resilient cultivation systems with the potential of increasing ecosystem services. Especially under temperate climatic conditions, the implementation in agricultural practice is low so far, inter alia due to the lack of knowledge regarding longer-term effects of such systems. This study investigated biomass yields and crop development during the second rotation of an alley cropping system with willows (clone “Tordis” ((Salix schwerinii x S. viminalis) x S. vim.)) and grassland that was established in March 2011, as reported in a former study of the authors. Two grassland swards (white clover grass (Lolium perenne L. and Trifolium repens L.) (CG) and a diversity mixture with 32 plant species (DIV)) were proven in an intensive (3/4 cuts per year) and extensive management system (two cuts per year). Total yield (sum of three years) of AFS increased substantially in the second rotation (year four to six after establishment of the AFS). This was particularly due to a fivefold increase in woody biomass. At the same time, yields of grassland biomass decreased slightly. Biomass of CG outperformed DIV, especially in the intensive managed systems with a dry matter (DM) yield of 18 t ha−1, compared to 12.6 t ha−1. However, AFS grassland yields were always lower than yields of reference areas with grassland in pure stand. Nevertheless, lower yields are probably caused by competition effects between woody crops and grassland. Grassland yields along transects across the grassland alleyways showed a strong decrease in the border areas in all treatments. Higher grassland yields in the alley center did not compensate yield reductions in border areas. Furthermore, the botanical composition of grassland was modified in border areas with reduced legume DM contribution and increases of both grasses and forbs. Thus, the width of grassland alleys with 9 m caused strong competition effects by the willows.

Remote sensing (RS) has been an effective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error < 10% was achieved. The growth stage of the crops had no significant effect on the prediction accuracy, although increasing trends of an underestimation of FMY with later sampling dates for eggplant and tomato were found. The study proves that an estimation of vegetable FMY using RS data is successful throughout the growing season. Different RS datasets were best for biomass prediction of the three vegetables, indicating that multi-sensory data collection should be preferred to single sensor use, as no one sensor system is superior.

The timely knowledge of forage quality of grasslands is vital for matching the demands in animal feeding. Remote sensing (RS) is a promising tool for estimating field-scale forage quality compared with traditional methods, which usually do not provide equally detailed information. However, the applicability of RS prediction models depends on the variability of the underlying calibration data, which can be brought about by the inclusion of a multitude of grassland types and management practices in the model development. Major aims of this study were (i) to build forage quality estimation models for multiple grassland types based on an unmanned aerial vehicle (UAV)-borne imaging spectroscopy and (ii) to generate forage quality distribution maps using the best models obtained. The study examined data from eight grasslands in northern Hesse, Germany, which largely differed in terms of vegetation type and cutting regime. The UAV with a hyperspectral camera on board was utilised to acquire spectral images from the grasslands, and crude protein (CP) and acid detergent fibre (ADF) concentration of the forage was assessed at each cut. Five predictive modelling regression algorithms were applied to develop quality estimation models. Further, grassland forage quality distribution maps were created using the best models developed. The normalised spectral reflectance data showed the strongest relationship with both CP and ADF concentration. From all predictive algorithms, support vector regression provided the highest precision and accuracy for CP estimation (median normalised root mean square error prediction (nRMSEp) = 10.6%), while cubist regression model proved best for ADF estimation (median nRMSEp = 13.4%). The maps generated for both CP and ADF showed a distinct spatial variation in forage quality values for the different grasslands and cutting regimes. Overall, the results disclose that UAV-borne imaging spectroscopy, in combination with predictive modelling, provides a promising tool for accurate forage quality estimation of multiple grasslands.

Activated carbon (AC), used for removal of organic micropollutants in European wastewater treatment plants (WWTPs), is usually produced from non-renewable resources that need to be transported over long distances. Utilising local residual biomass as a raw material may be advantageous in terms of sustainability. This study investigated the environmental and energy balances of using biowaste and biomass from landscape management for micropollutant removal at a commercial scale WWTP. Both residual biomasses were processed using the integrated generation of solid fuel and biogas from biomass (IFBB) technique to obtain a press cake that was used as feedstock for AC production. The results showed a lower global warming potential (GWP) and cumulative energy demand in comparison to a fossil-based conventional AC Differences in GWP between residual and fossil ACs were enhanced when the end-of-life incineration step was considered, and residual AC had a lower social risk associated with its production. Energy efficiency of AC production was substantially increased by utilising waste heat generated in the pyrolysis process of biochar production and by using electricity generated in a combined heat and power plant using biogas from the methanation of IFBB press fluids. Converting residual biomass into activated carbon using IFBB and a state-of-the-art pyrolysis and activation unit along with energy recovery would improve WWTP sustainability and self-sufficiency in terms of the raw materials required.

Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016−2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R² validation (R²val) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R²val = 0.53, rRMSEP = 13.9%), maize (R²val = 0.53, rRMSEP = 18.7%) and finger millet (R²val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546−910 nm (lablab), 750−794 nm (maize) and 686−814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth.

The spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.

An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R2 = 0.59⁻0.81) as the ruler height measurements (R2 = 0.58⁻0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types.

Biomass-based energy accounts for a notable share of renewable heat and electricity generation in Germany. Due to limited alternative uses, biomass obtained from management of semi-natural grasslands is a potential feedstock. Technical and environmental limitations exist in using this biomass for combustion, due to the presence of harmful elements. Converting biomass using integrated generation of solid fuel and biogas from biomass system (IFBB) produces a solid fuel with lower concentrations of harmful elements and a press liquid usable for biogas generation. In this study, solid fuel generation with a commercial scale IFBB unit was investigated. The concentration of harmful elements such as N, S, Cl, and K in the solid fuel was significantly reduced compared to the original biomass silage. Emissions during combustion of the solid fuel briquettes were below German legal thresholds. Elemental concentration of solid fuel obtained from commercial scale process had a significant improvement in removal rate of harmful elements than the prototype. Hence, the limitations of using semi-natural grassland biomass as an energy source were overcome. The commercial scale IFBB plant could be used in practice to handle large volumes of green residual biomass by converting it into a solid fuel with favorable fuel properties.

3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

Renewable energies are necessary to reduce greenhouse gas emissions in energy production, and biomass plays a dominant role in the renewable energy sector. Combustion of biomass constitutes an efficient conversion technique, but is hindered by harmful elements which are frequently contained in residual grassland biomass. We investigated the effect of leaching on solid fuel quality with three independent experiments including the following treatments: (1) with or without leaching, (2) with leaching at various water to silage ratios and (3) with the use of press liquid versus fresh water. Biomass was mechanically dehydrated and press cakes for combustion were produced and analyzed for their concentrations of ash, N and minerals harmful for combustion. Solid fuel quality was improved by leaching prior to dewatering, and the application of higher proportions of fresh water enabled even higher quality to be attained.

Sports fields provide a recreation space for citizens, but also generate grass biomass, which is cut weekly during the main seasons and therefore could be used in energy generation (combustion or anaerobic digestion). To evaluate the technical suitability of the grass cuttings, silage was produced from four sports fields during one vegetation period and investigated for relevant properties. Potential methane yield was determined with batch tests. Mean methane yield was 291.86 lN·kg−1 VSadded (VS, volatile solid). Neutral detergent fiber concentration was low (44.47% DM, dry matter), yet mineral concentration was high in comparison to grass types cut at a lower frequency. Concentrations of Cl, N, and S, which may lead to unfavorable emissions, fouling, and corrosion during combustion, were too high for an unproblematic combustion process. This was still the case even after applying a mineral-reducing pretreatment, which generates a fiber-rich press cake and a press fluid rich in easy soluble substances. Digestion of the press fluid led to methane yields of 340.10 lN·kg−1 VSadded and the press cake had a higher heating value of 19.61 MJ·kg−1 DM, which is close to that of coniferous wood. It can be concluded that biomass from sports fields could be a suitable co-substrate in bio-energy generation.

Vast amounts of leaf litter have to be disposed of by city administrations. This biomass has the potential for energy conversion, but contamination with pollutants can adversely affect this usage. We investigated leaf litter samples from the city of Kassel by analyzing their heavy metal and polycyclic aromatic hydrocarbon (PAH) concentrations. Leaf samples were indeed contaminated with heavy metals and PAHs and contamination was influenced by provenience and collection technique. A simple cleaning system of washing and subsequent mechanical dewatering significantly reduced heavy metal concentration. Regression models were developed for contamination with heavy metals which showed that contamination could be successfully estimated using the total ash content of the sample as a predictor, with an R2 of up to 0.77. It can be concluded that leaf litter from cities is a possible feedstock for energetic conversion, provided a cleaning step is applied.

An accurate estimation of biomass is needed to understand the spatio-temporal changes of forage resources in pasture ecosystems and to support grazing management decisions. A timely evaluation of biomass is challenging, as it requires efficient means such as technical sensing methods to assess numerous data and create continuous maps. In order to calibrate ultrasonic and spectral sensors, a field experiment with heterogeneous pastures continuously stocked by cows at three grazing intensities was conducted. Sensor data fusion by combining ultrasonic sward height (USH) with narrow band normalized difference spectral index (NDSI) (R2CV = 0.52) or simulated WorldView2 (WV2) (R2CV = 0.48) satellite broad bands increased the prediction accuracy significantly, compared to the exclusive use of USH or spectral measurements. Some combinations were even better than the use of the full hyperspectral information (R2CV = 0.48). Spectral regions related to plant water content were found to be of particular importance (996–1225 nm). Fusion of ultrasonic and spectral sensors is a promising approach to assess biomass even in heterogeneous pastures. However, the suggested technique may have limited usefulness in the second half of the growing season, due to an increasing abundance of senesced material.