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Effects of Drought and Heat on Photosynthetic Performance, Water Use and Yield of Two Selected Fiber Hemp Cultivars at a Poor-Soil Site in Brandenburg (Germany)
Hemp currently regains certain importance as fiber, oil and medical crop not least because of its modest requirements of biocides, fertilizer and water. During recent years, crops were exposed to a combination of drought and heat, even in northern Central-Europe. Dynamic responses of photosynthesis and stomatal conductance to these stresses and their persistent effects had been studied, if at all, in controlled environment experiments. Comprehensive field studies on diurnal and long-term net photosynthesis and gas exchange, and yield properties of hemp during a drought prone, high-temperature season in northern Central-Europe are obviously missing. Thus, in whole season field trails, the essential actual physiological (rates of net photosynthesis and transpiration, stomatal conductance, water use efficiencies, ambient and internal CO2 concentrations) and the yield performance of modern high-yielding multi-purpose hemp cultivars, ‘Ivory’ and ‘Santhica 27’, were evaluated under extreme environmental conditions and highly limited soil water supply. This provides comprehensive information on the usability of these cultivars under potential future harsh production conditions. Plants of both cultivars differentially cope with the prevailing climatic and soil water conditions. While ‘Ivory’ plants developed high rates of CO2 gain and established large leaf area per plant in the mid-season, those of ‘Santhica 27’ utilized lower CO2 uptake rates at lower leaf area per plant most time. This and the higher germination success of ‘Santhica 27’ resulted in nearly twice the yield compared to ‘Ivory’. Although stomatal control of CO2 gain was pronounced in both cultivars, higher stomatal limitations in ‘Ivory’ plants resulted in higher overall intrinsic water use efficiency. Cultivation of both hemp cultivars with only basic irrigation during seed germination was successful and without large effects on yield and quality. This was valid even under extremely hot and dry climatic conditions in northern Central Europe.
DNA and RNA extraction and quantitative real-time PCR-based assays for biogas biocenoses in an interlaboratory comparison
Five institutional partners participated in an interlaboratory comparison of nucleic acid extraction, RNA preservation and quantitative Real-Time PCR (qPCR) based assays for biogas biocenoses derived from different grass silage digesting laboratory and pilot scale fermenters. A kit format DNA extraction system based on physical and chemical lysis with excellent extraction efficiency yielded highly reproducible results among the partners and clearly outperformed a traditional CTAB/chloroform/isoamylalcohol based method. Analytical purpose, sample texture, consistency and upstream pretreatment steps determine the modifications that should be applied to achieve maximum efficiency in the trade-off between extract purity and nucleic acid recovery rate. RNA extraction was much more variable, and the destination of the extract determines the method to be used. RNA stabilization with quaternary ammonium salts was an as satisfactory approach as flash freezing in liquid N2. Due to co-eluted impurities, spectrophotometry proved to be of limited value for nucleic acid qualification and quantification in extracts obtained with the kit, and picoGreen® based quantification was more trustworthy. Absorbance at 230 nm can be extremely high in the presence of certain chaotropic guanidine salts, but guanidinium isothiocyanate does not affect (q)PCR. Absolute quantification by qPCR requires application of a reliable internal standard for which correct PCR efficiency and Y-intercept values are important and must be reported.
Impact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds
Estimation of plant canopy using low-altitude imagery can help monitor the normal growth status of crops and is highly beneficial for various digital farming applications such as precision crop protection. However, extracting 3D canopy information from raw images requires studying the effect of sensor viewing angle by taking into accounts the limitations of the mobile platform routes inside the field. The main objective of this research was to estimate wheat (Triticum aestivum L.) leaf parameters, including leaf length and width, from the 3D model representation of the plants. For this purpose, experiments with different camera viewing angles were conducted to find the optimum setup of a mono-camera system that would result in the best 3D point clouds. The angle-control analytical study was conducted on a four-row wheat plot with a row spacing of 0.17 m and with two seeding densities and growth stages as factors. Nadir and six oblique view image datasets were acquired from the plot with 88% overlapping and were then reconstructed to point clouds using Structure from Motion (SfM) and Multi-View Stereo (MVS) methods. Point clouds were first categorized into three classes as wheat canopy, soil background, and experimental plot. The wheat canopy class was then used to extract leaf parameters, which were then compared with those values from manual measurements. The comparison between results showed that (i) multiple-view dataset provided the best estimation for leaf length and leaf width, (ii) among the single-view dataset, canopy, and leaf parameters were best modeled with angles vertically at -45⸰_ and horizontally at 0⸰_ (VA -45, HA 0), while (iii) in nadir view, fewer underlying 3D points were obtained with a missing leaf rate of 70%. It was concluded that oblique imagery is a promising approach to effectively estimate wheat canopy 3D representation with SfM-MVS using a single camera platform for crop monitoring. This study contributes to the improvement of the proximal sensing platform for crop health assessment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
The Effect of Diet and Farm Management on N2O Emissions from Dairy Farms Estimated from Farm Data
The N2O emissions of 21 dairy farms in Germany were evaluated to determine the feasi-bility of an estimation of emissions from farm data and the effects of the farm management, along with possible mitigation strategies. Emissions due to the application of different fertilisers, manure storage and grazing were calculated based on equations from the IPCC (Intergovernmental Panel of Climate Change) and German emission inventory. The dependence of the N2O emissions on fertiliser type and quantity, cultivated crops and diet composition was assessed via correlation analysis and linear regression. The N2O emissions ranged between 0.11 and 0.29 kg CO2eq per kilogram energy-corrected milk, with on average 60% resulting from fertilisation and less than 30% from fertiliser storage and field applications. The total emissions had a high dependence on the diet composition; in particular, on the grass/maize ratio and the protein content of the animal diet, as well as from the manure management. A linear model for the prediction of the N2O emissions based on the diet composition and the fertilisation reached a predictive power of R2 = 0.89. As a possible mitigation strategy, the substitution of slurry for solid manure would reduce N2O emissions by 40%. Feeding cows maize-based diets instead of grass-based diets could reduce them by 14%. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India
False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India.
Can Green Plants Mitigate Ammonia Concentration in Piglet Barns?
For animal welfare and for farmers’ health, the concentration of ammonia (NH3 ) in animal houses should be as low as possible. Plants can remove various atmospheric contaminants through the leaf stomata. This study examined the effect of ornamental plants installed inside a piglet barn on the NH3 concentration in the air. Gas measurements of the air in the ‘greened’ compartment (P) and a control compartment (CTR) took place over two measuring periods (summer–autumn and winter). Differences between the NH3 emissions were calculated based on the ventilation rates according to the CO2 balance. Fairly low mean NH3 concentrations between 2 and 4 ppm were measured. The NH3 emissions were about 20% lower (p < 0.01) in P than in CTR, in summer–autumn and in winter period. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Particulate Matter Dispersion Modeling in Agricultural Applications: Investigation of a Transient Open Source Solver
Agriculture is a major emitter of particulate matter (PM), which causes health problems and can act as a carrier of the pathogen material that spreads diseases. The aim of this study was to investigate an open-source solver that simulates the transport and dispersion of PM for typical agricultural applications. We investigated a coupled Eulerian–Lagrangian solver within the open source software package OpenFOAM. The continuous phase was solved using transient large eddy simulations, where four different subgrid-scale turbulence models and an inflow turbulence generator were tested. The discrete phase was simulated using two different Lagrangian solvers. For the validation case of a turbulent flow of a street canyon, the flowfield could be recaptured very well, with errors of around 5% for the non-equilibrium turbulence models (WALE and dynamicKeq) in the main regions. The inflow turbulence generator could create a stable and accurate boundary layer for the mean vertical velocity and vertical profile of the turbulent Reynolds stresses R11. The validation of the Lagrangian solver showed mixed results, with partly good agreements (simulation results within the measurement uncertainty), and partly high deviations of up to 80% for the concentration of particles. The higher deviations were attributed to an insufficient turbulence regime of the used validation case, which was an experimental chamber. For the simulation case of PM dispersion from manure application on a field, the solver could capture the influence of features such as size and density on the dispersion. The investigated solver is especially useful for further investigations into time-dependent processes in the near-source area of PM sources.
Optical Spectrometry to Determine Nutrient Concentrations and other Physicochemical Parameters in Liquid Organic Manures: A Review
Nutrient concentrations in livestock manures and biogas digestates show a huge variability due to disparities in animal husbandry systems concerning animal species, feed composition, etc. Therefore, a nutrient estimation based on recommendation tables is not reliable when the exact chemical composition is needed. The alternative, to analyse representative fertilizer samples in a standard laboratory, is too time-and cost-intensive to be an accepted routine method for farmers. However, precise knowledge about the actual nutrient concentrations in liquid organic fertilizers is a prerequisite to ensure optimal nutrient supply for growing crops and on the other hand to avoid environmental problems caused by overfertilization. Therefore, spectrometric methods receive increasing attention as fast and low-cost alternatives. This review summarizes the present state of research based on optical spectrometry used at laboratory and field scale for predicting several parameters of liquid organic manures. It emphasizes three categories: (1) physicochemical parameters, e.g., dry matter, pH, and electrical conductivity; (2) main plant nutrients, i.e., total nitrogen, ammonium nitrogen, phosphorus, potassium, magnesium, calcium, and sulfur; and (3) micronutrients, i.e., manganese, iron, copper, and zinc. Furthermore, the commonly used sample preparation techniques, spectrometer types, measuring modes, and chemometric methods are presented. The primarily promising scientific results of the last 30 years contributed to the fact that near-infrared spectrometry (NIRS) was established in commercial laboratories as an alternative method to wet chemical standard methods. Furthermore, companies developed technical setups using NIRS for on-line applications of liquid organic manures. Thus, NIRS seems to have evolved to a competitive measurement procedure, although parts of this technique still need to be improved to ensure sufficient accuracy, especially in quality management.
Factors That Influence Nitrous Oxide Emissions from Agricultural Soils as Well as Their Representation in Simulation Models: A Review
Nitrous oxide (N2O) is a long-lived greenhouse gas that contributes to global warming. Emissions of N2O mainly stem from agricultural soils. This review highlights the principal factors from peer-reviewed literature affecting N2O emissions from agricultural soils, by grouping the factors into three categories: environmental, management and measurement. Within these categories, each impact factor is explained in detail and its influence on N2O emissions from the soil is summarized. It is also shown how each impact factor influences other impact factors. Process-based simulation models used for estimating N2O emissions are reviewed regarding their ability to consider the impact factors in simulating N2O. The model strengths and weaknesses in simulating N2O emissions from managed soils are summarized. Finally, three selected process-based simulation models (Daily Century (DAYCENT), DeNitrification-DeComposition (DNDC), and Soil and Water Assessment Tool (SWAT)) are discussed that are widely used to simulate N2O emissions from cropping systems. Their ability to simulate N2O emissions is evaluated by describing the model components that are relevant to N2O processes and their representation in the model.
Carbon Budget of an Agroforestry System after Being Converted from a Poplar Short Rotation Coppice
Poplar (Populus L. spp.) Short Rotation Coppice systems (SRCs) for bioenergy production are being converted back to arable land. Transitioning to Alley Cropping Systems (ACSs) could be a suitable strategy for integrating former tree rows and arable crops. A field trial (Pisa, Central Italy) was set up with the aim of assessing the C storage of an ACS system based on hybrid poplar and sorghum (Sorghum bicolor L. Moench) and comparing it with that of an SRC cultivation system. The carbon budget at the agroecosystem scale was assessed in the first year of the transition using the net biome production (NBP) approach with a simplified method. The overall NBP for the SRC was positive (96 ± 40 g C m−2 year−1), highlighting that the system was a net carbon sink (i.e., NBP > 0). However, the ACS registered a net C loss (i.e., NBP < 0), since the NBP was −93 ± 56 g C m−2 year−1. In the first year of the transition, converting the SRC into an ACS counteracted the potential beneficial effect of C storage in tree belowground biomass due to the high heterotrophic respiration rate recorded in the ACS, which was fostered by the incorporation of residues and tillage disturbance in the alley. Additional years of heterotrophic respiration measurements could allow for an estimate of the speed and extent of C losses.