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Retting of fibrous plants such as flax is an essential step in the extraction of fibre bundles and their transformation into textiles and reinforcement fabrics for use in garments and composites. Dew-retting is traditionally performed from Northwest France to the Netherlands, as the climate is highly favourable for this process. Hemp is a plant that can be grown almost all over Europe with a low environmental impact. A retting step is also required to facilitate the separation of the hemp fibres before their transformation into textiles for garments or for 1D to 3D reinforcement composites, which requires thoroughly separated fibres. Dew-retting is currently used in flax production zones. The present work demonstrates that dew retting can be conducted under different climates on different soils, from dry Mediterranean environments up to the cooler regions of eastern France. If the ternary combination of moisture, temperature and solar radiation is appropriate, field retting (dew-retting) can be as short as about three weeks. In less favourable conditions, such as in dryer areas or when retting is performed late in the season after seed maturity (cooler temperatures), it lasts longer, but it can reach suitable levels. When conducted with care and with proper monitoring of the retting level, the dew-retting process does impact neither the tensile properties of elementary hemp fibres (by degrading crystalline cellulose I) nor the tensile properties of unidirectional and injected composite materials. Consequently, if extracted with a suitable process such as scutching and hackling, fibres suitable for load-bearing composites can be produced from dew-retted hemp stems produced in a wide range of climates and locations, therefore not limited to the conventional “dew retting zone” of flax production areas.

Introducing smart and sustainable tools for climate change adaptation and mitigation is a major need to support agriculture’s productivity potential. We assessed the effects of the processed gypsum seed dressing SOP® COCUS MAIZE+ (SCM), combined with a gradient of N fertilization rates (i.e., 0%, 70% equal to 160 kg N ha−1, and 100% equal to 230 kg N ha−1) in maize (Zea mays L.), on: (i) grain yield, (ii) root length density (RLD) and diameter class length (DCL), (iii) biodiversity of soil bacteria and fungi, and (iv) Greenhouse Gases (GHGs, i.e., N2O, CO2, and CH4) emission. Grain yield increased with SCM by 1 Mg ha−1 (+8%). The same occurred for overall RLD (+12%) and DCL of very fine, fine, and medium root classes. At anthesis, soil microbial biodiversity was not affected by treatments, suggesting earlier plant-rhizosphere interactions. Soil GHGs showed that (i) the main driver of N losses as N2O is the N-fertilization level, and (ii) decreasing N-fertilization in maize from 100% to 70% decreased N2O emissions by 509 mg N-N2O m−2 y−1. Since maize grain yield under SCM with 70% N-fertilization was similar to that under Control with 100% N-fertilization, we concluded that under our experimental conditions SCM may be used for reducing N input (−30%) and N2O emissions (−23%), while contemporarily maintaining maize yield. Hence, SCM can be considered an available tool to improve agriculture’s alignment to the United Nation Sustainable Development Goals (UN SDGs) and to comply with Europe’s Farm to Fork strategy for reducing N-fertilizer inputs.

Unmanned aerial vehicle (UAV) remote sensing was used to estimate the leaf area index (LAI) and leaf chlorophyll content (LCC) of two hemp cultivars during two growing seasons under four nitrogen fertilisation levels. The hemp traits were estimated by the inversion of the PROSAIL model from UAV multispectral images. The look-up table (LUT) and hybrid regression inversion methods were compared. The hybrid methods performed better than LUT methods, both for LAI and LCC, and the best accuracies were achieved by random forest for the LAI (0.75 m2 m−2 of RMSE) and by Gaussian process regression for the LCC (9.69 µg cm−2 of RMSE). High-throughput phenotyping was carried out by applying a generalised additive model to the time series of traits estimated by the PROSAIL model. Through this approach, significant differences in LAI and LCC dynamics were observed between the two hemp cultivars and between different levels of nitrogen fertilisation.

Hemp (Cannabis sativa L.) is a multi-purpose crop that finds applications in technical textile, paper, food, cosmetic and automotive industries. Due to the increasing demand for sustainable fibres, the hemp fibre sector is expected to grow in the future. However, overall information on the effect of agronomic practices on decortication efficiency and hemp fibre quality traits is very limited. This study aims to evaluate the relationship between agro-technique, decortication process, and energy consumption properties. In this paper, four planting densities, four levels of nitrogen fertilisation and two harvesting times were compared in different environments to evaluate the effect of agronomy on stem and fibre processing properties, including energy requirements, using a lab-scaled hemp stem decorticator. The present work highlights that the optimal plant density and nitrogen fertilisation levels to reach high decortication efficiency are 120 plants m(-2) and from 0 to 100 Kg N ha(-1), respectively. The effect of nitrogen and density on the considered properties is mediated by their influence on stem diameter. The highest decortication efficiency and the highest efficiency in energy use were observed at full flowering. On the contrary the fineness of the fibre, which was only evaluated in one environment, was highest in stems harvested at seed maturity, in particular when plant densities above 120 plants m(-2) and when 60-100 Kg N ha(-1) were applied. The methodology used in this work effectively evaluates the impact of agro-technique on the efficiency and energy requirements of hemp decortication.

Near-real-time, high-spatial-resolution leaf area index (LAI) maps would enable producers to monitor crop health and growth status, improving agricultural practices such as fertiliser and water management. LAI retrieval methods are numerous and can be divided into statistical and physically based methods. While statistical methods are generally subject to high site-specificity but possess high ease of implementation and use, physically based methods are more transferable, albeit more complex to use in operational settings. In addition, statistical methods need a large amount of data for calibration and subsequent validation, and this is only seldom feasible. Techniques based on predictive equations (PEphysical) represent a viable alternative, allowing the partial combination of statistical and physical methods merits while minimising their shortcomings. In this paper, predictive equation-based techniques were compared with four other methods: two radiative transfer model (RTM) inversion methods, one based on neural network (NNET) and one based on a look-up table (LUT), and two empirical methods (one using empirical models based on vegetation indices and in situ data and one based on empirical models found in the scientific literature). The methods were chosen based on common use. To evaluate the performance of the studied methods, the coefficient of determination (R2), root mean square error (RMSE), and normalised root mean square error (nRMSE, %) between the estimates and in situ LAI measurements were reported. The best PEphysical results, achieved by the OSAVI index (RMSE = 0.84 m2 m−2), provided better performance for LAI recovery than the NNET-based RTM inversions (0.86 m2 m−2) or the estimates made by LUT (0.94 m2 m−2). Furthermore, the best PEphysical produced accuracies comparable to the best empirical model (RMSE = 0.71 m2 m−2), calibrated through in situ data, and similar to the best literature model (RMSE = 0.76 m2 m−2). These results indicated that PEphys

Agrivoltaic systems (AV) combine agricultural activities with the production of electricity from photovoltaic (PV) panels on the same land area. The concept of AV systems was introduced in 1982 by Goetzberger and Zastrow, but only more recently have the increased environmental concerns and the favorable economic and political frameworks stimulated a growing interest in this technology. A critical issue in the development of AV is the selection of crops that can grow profitably under the micrometeorological conditions generated by AV systems. This experiment studied the effect of four different shade depth treatments (AV1 = 27%, AV2 = 16%, AV3 = 9%, and AV4 = 18%) on the morphology, physiology, and yield of a soybean crop grown under a large-scale AV system. The field results were used to validate the output of a simulation platform that couples the crop model GECROS with a set of algorithms for the estimation and spatialisation of the shading, radiation, and crop-related outputs. Crop height, leaf area index (LAI), and specific leaf area (SLA) all increased under the most shaded AV areas compared to the full light (FL, control) conditions. On average, under an AV system, the grain yield and the number of pods per plant were reduced by 8% and 13%, and in only one area (AV2) was a slight increase in grain yield (+4.4%) observed in comparison to the FL. The normalised root mean square error (nRMSE) value of the predicted grain yield differed from the observed grain values of 12.9% for the FL conditions, 15.7% in AV1, 16.5% in AV2, 6.71% in AV3, and 2.82% in AV4. Although the model simulated the yield satisfactorily, the results of the RMSE revealed that the model tends to underestimate the yield with an increase in shade, particularly for the AV1 and AV2 conditions.

Estimating key crop parameters (e.g., phenology, yield prediction) is a prerequisite for optimizing agrifood supply chains through the use of satellite imagery, but requires timely and accurate crop mapping. The moment in the season and the number of training sites used are two main drivers of crop classification performance. The combined effect of these two parameters was analysed for tomato crop classification, through 125 experiments, using the three main machine learning (ML) classifiers (neural network, random forest, and support vector machine) using a response surface methodology (RSM). Crop classification performance between minority (tomato) and majority (‘other crops’) classes was assessed through two evaluation metrics: Overall Accuracy (OA) and G-Mean (GM), which were calculated on large independent test sets (over 400,000 fields). RSM results demonstrated that lead time and the interaction between the number of majority and minority classes were the two most important drivers for crop classification performance for all three ML classifiers. The results demonstrate the feasibility of preharvest classification of tomato with high performance, and that an RSM-based approach enables the identification of simultaneous effects of several factors on classification performance. SVM achieved the best grading performances across the three ML classifiers, according to both evaluation metrics. SVM reached highest accuracy (0.95 of OA and 0.97 of GM) earlier in the season (low lead time) and with less training sites than the other two classifiers, permitting a reduction in cost and time for ground truth collection through field campaigns.

Decomposition models of solar irradiance estimate the magnitude of diffuse horizontal irradiance from global horizontal irradiance. These two radiation components are well known to be essential for predicting the performance of solar photovoltaic systems. In open-field agrivoltaic systems (i.e., the dual use of land for both agricultural activities and solar power conversion), cultivated crops receive unequal amounts of direct, diffuse, and reflected photosynthetically active radiation (PAR). These uneven amounts depend on where the crops are growing due to the non-homogenous shadings caused by the presence of the installed solar panels (above the crops or vertically mounted). It is known that, per unit of total PAR, diffuse PAR is more efficient for canopy photosynthesis than is direct PAR. For this reason, it is essential to estimate the diffuse PAR component when agrivoltaic systems are being assessed, in order to properly predict the crop yield. Since PAR is the electro-magnetic radiation in the 400-700 nm waveband that can be used for photosynthesis by the crops, several stand-alone decomposition models typically used to split global horizontal irradiance are selected in this study to decompose PAR into direct and diffuse. These models are applied and validated in three locations in Sweden (Lanna, Hyltemossa and Norunda) using the coefficients stated on the models' original publications and locally fitted coefficients. The results showed weaker performances in all stand-alone models for non-locally fitted coefficients (nRMSE ranging from 27% to 43%). However, performances improve with re-parameterization, with a highest nRMSE of 35.24% in Lanna. The Y(ANG)2 decomposition model is the best-performing one, with the lowest nRMSE of 23.75% in Norunda when applying re-estimated coefficients. Country level sets of coefficients for the best-performing models (Y(ANG)2 and STARKE) are given after parameterization using combined data for all three locations in Sweden. Th

Miscanthus, a C-4 perennial rhizomatous grass, is capable of growing in varied climates and soil types in Europe, including on marginal lands. It can produce high yields with low nutrient inputs when harvested after complete senescence. Senescence induction and rate depend on complex genetic, environmental, and management interactions. To explore these interactions, we analysed four miscanthus hybrids (two novel seed-based hybrids, GRC 3 [Miscanthus sinensis x sinensis] and GRC 14 [M. sacchariflorus x sinensis]; GRC 15, a novel M. sacchariflorus x sinensis clone; and GRC 9, a standard Miscanthus x giganteus clone) in Italy, Croatia, Germany and the UK. Over all trial locations and hybrids, the average aboveground biomass of the 3-year-old stands in August 2020 was 15 t DM ha(-1) with nutrient contents of 7.6 mg N g(-1) and 14.6 mg K g(-1). As expected, delaying the harvest until spring reduced overall yield and nutrient contents (12 t DM ha(-1), 3.3 mg N g(-1), and 5.5 mg K g(-1)). At lower latitudes, the late-ripening M. sacchariflorus x sinensis GRC 14 and GRC 15 combined high yields with low nutrient contents. At the most elevated latitude location (UK), the early-ripening M. sinensis x sinensis combined high biomass yields with low nutrient offtakes. The clonal Miscanthus x giganteus with intermediate flowering and senescence attained similar low nutrient contents by spring harvest at all four locations. Seasonal changes in yield and nutrient levels analysed in this study provide: (1) a first step towards recommending hybrids for specific locations and end uses in Europe; (2) crucial data for determination of harvest time and practical steps in the valorization of biomass; and (3) key sustainability data for life cycle assessments. Identification of trade-offs resulting from genetic x environment x management interactions is critical for increasing sustainable biomass supply from miscanthus grown on marginal lands.

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using a VIs time series, and predicted yield using a peak descriptor derived from a VIs time series with 2.3 Mg DM ha−1 of the root mean square error (RMSE). The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production.

The stinging nettle Urtica dioica L. is a perennial crop with low fertilizer and pesticide requirements, well adapted to a wide range of environmental conditions. It has been successfully grown in most European climatic zones while also promoting local flora and fauna diversity. The cultivation of nettle could help meet the strong increase in demand for raw materials based on plant fibers as a substitute for artificial fibers in sectors as diverse as the textile and automotive industries. In the present review, we present a historical perspective of selection, harvest, and fiber processing features where the state of the art of nettle varietal selection is detailed. A synthesis of the general knowledge about its biology, adaptability, and genetics constituents, highlighting gaps in our current knowledge on interactions with other organisms, is provided. We further addressed cultivation and processing features, putting a special emphasis on harvesting systems and fiber extraction processes to improve fiber yield and quality. Various uses in industrial processes and notably for the restoration of marginal lands and avenues of future research on this high-value multi-use plant for the global fiber market are described.

Miscanthus is a leading perennial biomass crop that can produce high yields on marginal lands. Moisture content is a highly relevant biomass quality trait with multiple impacts on efficiencies of harvest, transport, and storage. The dynamics of moisture content during senescence and overwinter ripening are determined by genotype × environment interactions. In this paper, unmanned aerial vehicle (UAV)-based remote sensing was used for high-throughput plant phenotyping (HTPP) of the moisture content dynamics during autumn and winter senescence of 14 contrasting hybrid types (progeny of M. sinensis x M. sinensis [M. sin x M. sin, eight types] and M. sinensis x M. sacchariflorus [M. sin x M. sac, six types]). The time series of moisture content was estimated using machine learning (ML) models and a range of vegetation indices (VIs) derived from UAV-based remote sensing. The most important VIs for moisture content estimation were selected by the recursive feature elimination (RFE) algorithm and were BNDVI, GDVI, and PSRI. The ML model transferability was high only when the moisture content was above 30%. The best ML model accuracy was achieved by combining VIs and categorical variables (5.6% of RMSE). This model was used for phenotyping senescence dynamics and identifying the stay-green (SG) trait of Miscanthus hybrids using the generalized additive model (GAM). Combining ML and GAM modeling, applied to time series of moisture content values estimated from VIs derived from multiple UAV flights, proved to be a powerful tool for HTPP.

Perennial crops have been proposed as a solution to couple the production of sustainable biomass for multiple uses with several environmental benefits such as soil C storage. Concerns exist that the C sequestered in soil could be lost in a few years after the perennial crops are reverted to arable land. In this study, the current knowledge on the effects of perennial crop reversion on soil C and N was summarized by performing a meta-analysis. One year after the reversion a significant increase of soil C and N stocks (+15% and +12% respectively) were found in the 0–30 cm layer, while in the time interval between the second to fifth year after the reversion, there were no significant increases or decreases of soil C and N. The incorporation of the belowground biomass (BGB) into the soil at reversion plays a key role in the fate of soil C and N stocks after the reversion. In fact, when reverting a multiannual biomass crop there are significant losses of soil C and N. In contrast, when reverting a perennial biomass crop (PBCs) such as rhizomatous herbaceous or SRC woody crops there are no losses of soil C and N. The BGB of perennial grass is mainly composed of root systems and not of a huge amount of belowground organs as in the case of PBCs. The shredding of the BGB and its transformation as particulate organic matter (POM) represent the major pulse C input at the reversion that can undergo further stabilization into a mineral-associated organic matter (MAOM) fraction. Introducing PBCs into crop rotation resulted in an effective carbon farming solution with a potential positive legacy for food crops in terms of achievement of both climate and soil fertility goals.

Hemp is a sustainable source of natural fibres that can contribute to meet the increasing demand for technical applications in the textile and the composite sectors. Continuous reinforcements can be produced using the existing flax machinery, initially developed for textile purposes. To achieve competitive and economically viable fibre yields and a fibre quality suitable for secondary processing and composite application, hemp needs to be adequately selected and prepared and the flax machinery and settings have to be adapted to the hemp specificities. In this context, this paper studies the influence of agronomic features and processing stages and settings on the effective tensile properties of fibres extracted from two hemp varieties determined using impregnated fibre bundle tests. Results show that the effective properties of fibres are maintained and even improved during processing, in particular during the hackling and stretching steps. Hemp can achieve properties comparable to high quality long flax fibres.

Genetic markers can be used in seeds and in plants to distinguish drug-type from fiber-type Cannabis Sativa L. varieties even at early stages, including pre-germination when cannabinoids are not accumulated yet. With this aim, this paper reports sequencing results for tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS) genes from 21 C. sativa L. varieties. Taking into account that THCAS-and CBDAS-derived enzymes compete for the same substrate, the novelty of this work relies in the identification of markers based on both THCAS and CBDAS rather than THCAS alone. Notably, in our panel, we achieved an adequate degree of discrimination (AUC 100%) between drug-type and fiber-type cannabis samples. Our sequencing approach allowed identifying multiple genetic markers (single-nucleotide polymorphisms—SNPs—and a deletion/insertion) that effectively discriminate between the two subgroups of cannabis, namely fiber type vs. drug type. We identified four functional SNPs that are likely to induce decreased THCAS activity in the fiber-type cannabis plants. We also report the finding on a deletion in the CBDAS gene sequence that produces a truncated protein, possibly resulting in loss of function of the enzyme in the drug-type varieties. Chemical analyses for the actual concentration of cannabinoids confirmed the identification of drug-type rather than fiber-type genotypes. Genetic markers permit an early identification process for forensic applications while simplifying the procedures related to detection of therapeutic or industrial hemp.

An accurate measurement of the 140Ce(n,g) energy-dependent cross-section was performed at the n_TOF facility at CERN. This cross-section is of great importance because it represents a bottleneck for the s-process nucleosynthesis and determines to a large extent the cerium abundance in stars. The measurement was motivated by the significant difference between the cerium abundance measured in globular clusters and the value predicted by theoretical stellar models. This discrepancy can be ascribed to an overestimation of the 140Ce capture cross-section due to a lack of accurate nuclear data. For this measurement, we used a sample of cerium oxide enriched in 140Ce to 99.4%. The experimental apparatus consisted of four deuterated benzene liquid scintillator detectors, which allowed us to overcome the difficulties present in the previous measurements, thanks to their very low neutron sensitivity. The accurate analysis of the p-wave resonances and the calculation of their average parameters are fundamental to improve the evaluation of the 140Ce Maxwellian-averaged cross-section.

Hemp (Cannabis sativa L.) is a multi-purpose industrial species, which is valued as a low-input crop suited to marginal environments. However, research on the water stress resistance and water use efficiency of hemp cultivars is scarce. Three lab-scale experiments were carried out to test 26 commercial European hemp cultivars under osmotic stress at germination (experiment 1) and on plantlets at vegetative stage (experiment 2); and to calculate transpiration efficiency (TE) under well-watered conditions (experiment 3). Eight parameters were analysed: maximum germination percentage, lag time and germination homogeneity in experiment 1; maximum quantum yield of PSII (Fv/Fm), performance index, relative water content and proline content in experiment 2; TE in experiment 3. Osmotic stress application significantly affected all parameters in experiments 1 and 2, and responses to water stress differed significantly among cultivars for all parameters, except proline content. Considering all parameters except proline content, a principal component analysis was carried out for ranking the cultivars. The cultivars 'Carmaleonte', 'Codimono', 'Futura 75' and Santhica '70' ranked the highest, while cultivars 'Bialobrzeskie', 'Ermes' and 'Finola' had the worst performances for all traits.

The objective of this work was to assess the environmental performances of advanced biofuels produced from perennial energy crops (miscanthus and willow) grown in bioenergy buffer strips (BBS) and compare them with the environmental performances of alternative systems providing the same function, i.e. private mobility. The growing evidence of potentially negative environmental impacts of bioenergy pathways calls for renewed efforts in identifying win-win bioenergy pathways, thus capable of mitigating climate change without worsening other environmental impacts. An holistic approach encompassing all the relevant areas of environmental concern is thus fundamental to highlight environmental trade-offs. Therefore, in this study we follow an attributional Life Cycle Assessment approach, but our analysis includes detailed modelling of biogenic carbon pools, nutrients cycles, infrastructures’ impacts as well as the expansion of the system boundaries to include the fuel use. We find that the fragmented and linear configuration of the buffer strips does not affect significantly the GHG emissions of lignocellulosic ethanol for BBS compared to growing the crops in open field. Additionally, we find that ethanol from perennials grown in BBS has the potential to reduce several other environmental impacts associated to private mobility. Firstly, the cultivation of miscanthus and willow in BBS enables both the removal of nutrients from the environment and the removal of carbon from the atmosphere, through the creation of an additional terrestrial sink. Secondly, when compared to the use of fossil gasoline, bioethanol from BBS crops generates lower impacts on all other areas of environmental concern, such as resources depletion or air pollution. We also find that cars fuelled with bioethanol form buffer strips perform even better than electric vehicles in all the impact categories analysed except for acidification and particulate matter emissions, where battery electric vehicles run

Increasing the production of high-performance natural fibres that minimise their impact on the environment is a challenge that flax (Linum usitatissinum L.) cannot address alone. In flax traditional production territories, hemp (Cannabis sativa L.) can be a complementary source of high added value fibres if their yield of long line fibres can be maximised to levels equivalent to the one of flax. The objective of the present work was to establish process parameters maximising the long line fibre yield using flax dedicated scutching and hackling devices. A lab-scale scutching/hackling device was used to establish sets of process parameters which best improve the long fibre scutching yield and as a consequence minimise the production of tow fibres. Decreases in straw processing transfer and beating speeds during scutching were necessary so that to be less aggressive on the straw and fibres. Very high long fibre yields were obtained after scutching and hackling at the laboratory scale (18 % of the hemp straw mass). These very high results, combined to high straw yield production in the field indicate that hemp can be a very productive source of high-performance fibres as these ones showed tensile properties completely suitable for a textile use as well as for load bearing composite materials. If the potential of high production yields and high mechanical and morphological properties was demonstrated at the lab-scale, this one should be improved at the industrial scale. Suggestions to reach this goal are provided to prevent too high transformation of long fibres into tows and to keep the mechanical potential maximum. When using optimised parameters and a lab-scale scutching/hackling device, it was demonstrated that hemp has the potential for providing equivalent amounts of long fibres per hectare than flax with tensile properties about 20 % lower than the ones of flax.