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- ItemOptical Spectrometry to Determine Nutrient Concentrations and other Physicochemical Parameters in Liquid Organic Manures: A Review(Basel : MDPI, 2022) Horf, Michael; Vogel, Sebastian; Drücker, Harm; Gebbers, Robin; Olfs, Hans-WernerNutrient 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.
- ItemSimulating Cotton Growth and Productivity Using AquaCrop Model under Deficit Irrigation in a Semi-Arid Climate(Basel : MDPI AG, 2022) Aziz, Marjan; Rizvi, Sultan Ahmad; Sultan, Muhammad; Bazmi, Muhammad Sultan Ali; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Imran, Muhammad A.AquaCrop is a water-driven model that simulates the effect of environment and management on crop production under deficit irrigation. The model was calibrated and validated using three databases and four irrigation treatments (i.e., 100%ET, 80%ET, 70%ET, and 50%ET). Model performance was evaluated by simulating canopy cover (CC), biomass accumulation, and water productivity (WP). Statistics of root mean square error (RMSE) and Willmott’s index of agreement (d) showed that model predictions are suitable for non-stressed and moderate stressed conditions. The results showed that the simulated biomass and yield were consistent with the measured values with a coefficient of determination (R2) of 0.976 and 0.950, respectively. RMSE and d-index values for canopy cover (CC) were 2.67% to 4.47% and 0.991% to 0.998% and for biomass were 0.088 to 0.666 ton/ha and 0.991 to 0.999 ton/ha, respectively. Prediction of simulated and measured biomass and final yield was acceptable with deviation ˂10%. The overall value of R2 for WP in terms of yield was 0.943. Treatment with 80% ET consumed 20% less water than the treatment with 100%ET and resulted in high WP in terms of yield (0.6 kg/m3) and biomass (1.74 kg/m3), respectively. The deviations were in the range of −2% to 11% in yield and −2% to 4% in biomass. It was concluded that AquaCrop is a useful tool in predicting the productivity of cotton under different irrigation scenarios.
- ItemParticulate Matter Dispersion Modeling in Agricultural Applications: Investigation of a Transient Open Source Solver(Basel : MDPI, 2021) Janke, David; Swaminathan, Senthilathiban; Hempel, Sabrina; Kasper, Robert; Amon, ThomasAgriculture 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.
- ItemComparison of Methane Emission Patterns from Dairy Housings with Solid and Slatted Floors at Two Locations(Basel : MDPI, 2022) Hempel, Sabrina; Janke, David; Losand, Bernd; Zeyer, Kerstin; Zähner, Michael; Mohn, Joachim; Amon, Thomas; Schrade, SabineMethane (CH4) emissions from dairy husbandry are a hot topic in the context of active climate protection, where housing systems with slatted floors and slurry storage inside are in general expected to emit more than systems with solid floors. There are multiple factors, including climate conditions, that modulate the emission pattern. In this study, we investigated interrelations between CH4 emission patterns and climate conditions as well as differences between farm locations versus floor effects. We considered three data sets with 265, 264 and 275 hourly emission values from two housing systems (one slatted, one solid floor) in Switzerland and one system with solid floors in Germany. Each data set incorporated measurements in summer, winter and a transition season. The average CH4 emission was highest for the slatted floor system. For the solid floor systems, CH4 emissions at the Swiss location were around 30% higher compared to the German location. The shape of the distributions for the two solid floor systems was rather similar but very different from the distribution for the slatted floor system, which showed higher prevalence for extreme emissions. Rank correlations, which measure the degree of similarity between two rankings in terms of linear relation, were not able to detect dependencies at the selected significance level. In contrast, mutual information, which measures more general statistical dependencies in terms of shared information, revealed highly significant dependencies for almost all variable pairs. The weakest statistical relation was found between winds speed and CH4 emission, but the convection regime was found to play a key role. Clustering was consistent among the three data sets with five typical clusters related to high/low temperature and wind speed, respectively, as well as in some cases to morning and evening hours. Our analysis showed that despite the disparate and often insignificant correlation between environmental variables and CH4 emission, there is a strong relation between both, which shapes the emission pattern in many aspects much more in addition to differences in the floor type. Although a clear distinction of high and low emission condition clusters based on the selected environmental variables was not possible, trends were clearly visible. Further research with larger data sets is advisable to verify the detected trends and enable prognoses for husbandry systems under different climate conditions.
- ItemScientific Irrigation Scheduling for Sustainable Production in Olive Groves(Basel : MDPI AG, 2022) Aziz, Marjan; Khan, Madeeha; Anjum, Naveeda; Sultan, Muhammad; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Balasundram, Siva K.; Aleem, MuhammadThe present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R2 ) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency.
- ItemGreenhouse Gas Mitigation Costs of Reduced Nitrogen Fertilizer(Basel : MDPI AG, 2022) Meyer-Aurich, Andreas; Karatay, Yusuf NadiThe reduction of nitrogen (N) fertilizer use is a possible greenhouse gas (GHG) mitigation option, whereas cost estimation highly depends on assumptions of the yield response function. This paper analyzes the potential and range of GHG mitigation costs with reduced N fertilizer application based on empirical yield response data for winter rye (Secale cereale L.) and rapeseed (Brassica napus L.) from field experiments from 2013 to 2020 in Brandenburg, Germany. The field experiments included four to five N rates as mineral fertilizer treatments. Three different functional forms (linear-plateau, quadratic, and quadratic-plateau) were estimated to model yield response as a function of N supply. Economic calculations were based on relevant price–cost ratios. The results indicate that the opportunity costs of applying less fertilizer and the resulting GHG mitigation thereof vary in a great range across the years and crops estimated by different yield response functions. The linear-plateau function predominantly results in lower GHG mitigation costs than the quadratic and the quadratic-plateau function. On average, over eight years, a moderate reduction of N fertilizer (up to 20 kg/ha) offers a cost-efficient option for mitigating GHG emissions below EUR 50 per ton of CO2eq, even resulting in net profit gain in some cases.
- ItemImpact of Cultivation and Origin on the Fruit Microbiome of Apples and Blueberries and Implications for the Exposome(Berlin : Springer, 2022) Wicaksono, Wisnu Adi; Buko, Aisa; Kusstatscher, Peter; Cernava, Tomislav; Sinkkonen, Aki; Laitinen, Olli H.; Virtanen, Suvi M.; Hyöty, Heikki; Berg, GabrieleVegetables and fruits are a crucial part of the planetary health diet, directly affecting human health and the gut microbiome. The objective of our study was to understand the variability of the fruit (apple and blueberry) microbiome in the frame of the exposome concept. The study covered two fruit-bearing woody species, apple and blueberry, two countries of origin (Austria and Finland), and two fruit production methods (naturally grown and horticultural). Microbial abundance, diversity, and community structures were significantly different for apples and blueberries and strongly influenced by the growing system (naturally grown or horticultural) and country of origin (Austria or Finland). Our results indicated that bacterial communities are more responsive towards these factors than fungal communities. We found that fruits grown in the wild and within home gardens generally carry a higher microbial diversity, while commercial horticulture homogenized the microbiome independent of the country of origin. This can be explained by horticultural management, including pesticide use and post-harvest treatments. Specific taxonomic indicators were identified for each group, i.e., for horticultural apples: Pseudomonas, Ralstonia, and Stenotrophomonas. Interestingly, Ralstonia was also found to be enriched in horticultural blueberries in comparison to such that were home and wildly grown. Our study showed that the origin of fruits can strongly influence the diversity and composition of their microbiome, which means that we are exposed to different microorganisms by eating fruits from different origins. Thus, the fruit microbiome needs to be considered an important but relatively unexplored external exposomic factor.
- ItemPeptaibol Production and Characterization from Trichoderma asperellum and Their Action as Biofungicide(Basel : MDPI, 2022) Alfaro-Vargas, Pamela; Bastos-Salas, Alisson; Muñoz-Arrieta, Rodrigo; Pereira-Reyes, Reinaldo; Redondo-Solano, Mauricio; Fernández, Julián; Mora-Villalobos, Aníbal; López-Gómez, José PabloPeptaibols (Paib), are a class of biologically active peptides isolated from soil, fungi and molds, which have interesting properties as antimicrobial agents. Paib production was optimized in flasks by adding sucrose as a carbon source, 2-aminoisobutyric acid (Aib) as an additive amino acid, and F. oxysporum cell debris as an elicitor. Paib were purified, sequenced and identified by High-performance liquid chromatography (HPLC)coupled to mass spectrometry. Afterward, a Paib extract was obtained from the optimized fermentations. The biological activity of these extracts was evaluated using in vitro and in vivo methods. The extract inhibited the growth of specific plant pathogens, and it showed inhibition rates similar to those from commercially available fungicides. Growth inhibition rates were 92.2, 74.2, 58.4 and 36.2% against Colletotrichum gloeosporioides, Botrytis cinerea, Alternaria alternata and Fusarium oxysporum, respectively. Furthermore, the antifungal activity was tested in tomatoes inoculated with A. alternata, the incidence of the disease in tomatoes treated with the extract was 0%, while the untreated fruit showed a 92.5% incidence of infection Scanning electron microscopy images showed structural differences between the fungi treated with or without Paib. The most visual alterations were sunk and shriveled morphology in spores, while the hyphae appeared to be fractured, rough and dehydrated.
- ItemReadiness for Innovation of Emerging Grass-Based Businesses(Basel : MDPI, 2022) Orozco, Richard; Grundmann, PhilippNew business opportunities based on grassland and green fodder present a promising avenue to realize the transition towards a circular and sustainable bio-based economy. Yet, such potential remains largely untapped and grass-based products and businesses remain a small niche in the global economy. To understand this phenomenon, this paper introduces and operationalizes a model to assess innovation readiness built around seven focus areas: technology, manufacturing, business, IPR, customer, team, and funding readiness with their own detailed “progress scales.” We employ necessary condition analysis (NCA) to identify limiting factors and bottlenecks in actual business situations. Our results reveal that lack of consumer awareness, infant conversion technologies and paucity of long-term investments that support emerging bio-based businesses are the most limiting conditions for the growth of emerging grass-based markets. The present study advances our understanding of the factors that limit complex innovations in grassland systems. Focusing on necessary conditions in a coordinated way between practitioners and policy makers by giving priority to fostering positive awareness of bioeconomy businesses, developing conversion technologies, and improving access to capital is a recommended approach to foster emerging grass-based innovations.
- ItemComprehensive Assessment of the Dynamics of Banana Chilling Injury by Advanced Optical Techniques(Basel : MDPI, 2021) Herppich, Werner B.; Zsom, TamásGreen‐ripe banana fruit are sensitive to chilling injury (CI) and, thus, prone to postharvest quality losses. Early detection of CI facilitates quality maintenance and extends shelf life. CI affects all metabolic levels, with membranes and, consequently, photosynthesis being primary targets. Optical techniques such as chlorophyll a fluorescence analysis (CFA) and spectroscopy are promising tools to evaluate CI effects in photosynthetically active produce. Results obtained on bananas are, however, largely equivocal. This results from the lack of a rigorous evaluation of chilling impacts on the various aspects of photosynthesis. Continuous and modulated CFA and imaging (CFI), and VIS remission spectroscopy (VRS) were concomitantly applied to noninvasively and comprehensively monitor photosynthetically relevant effects of low temperatures (5 °C, 10 °C, 11.5 °C and 13 °C). Detailed analyses of chilling‐related variations in photosynthetic activity and photoprotection, and in contents of relevant pigments in green‐ripe bananas, helped to better understand the physiological changes occurring during CI, highlighting that distinct CFA and VRS parameters comprehensively reflect various effects of chilling on fruit photosynthesis. They revealed why not all CFA parameters can be applied meaningfully for early detection of chilling effects. This study provides relevant requisites for improving CI monitoring and prediction.