Yield Distribution Research Articles

Development of a novel computational technique to create DNA and cell geometrical models for Geant4-DNA

BackgroundThis study aimed to develop a novel human cell geometry for the Geant4-DNA simulation toolkit that explicitly incorporates all 23 chromosome pairs of the human cell. This approach contrasts with the existing, default human cell, geometrical model, which utilizes a continuous Hilbert curve. MethodsA Python-based tool named “complexDNA” was developed to facilitate the design of both simple and complex DNA geometries. This tool was employed to construct a human cell geometry with individual pairs of chromosomes. Subsequently, the performance of this chromosomal model was compared to the standard human cell model provided in the “molecularDNA” Geant4-DNA example. ResultsSimulations using the new chromosomal model revealed minimal discrepancies in DNA damage yield and fragment size distribution compared to the default human cell model. Notably, the chromosomal model demonstrated significant computational efficiency, requiring approximately three times less simulation time to achieve equivalent results. ConclusionsThis work highlights the importance of incorporating chromosomal structure into human cell models for radiation biology research. The “complexDNA” tool offers a valuable resource for creating intricate DNA structures for future studies. Further refinements, such as implementing smaller voxels for euchromatin regions, are proposed to enhance the model’s accuracy.

Validation of QTLs associated with corn borer resistance and grain yield: implications in maize breeding

IntroductionValidations of previously detected quantitative trait loci (QTLs) to assess their reliability are crucial before implementing breeding programs. The objective of this study was to determine the reliability and practical usefulness of previously reported QTLs for resistance to stem tunneling by the Mediterranean stem borer (MSB) and yield. These authors used approximately 600 recombinant inbred lines (RILs) from a multiparent advanced generation intercross (MAGIC) population to map QTL using a genome-wide association study (GWAS) approach.MethodsWe identified RILs situated at the extremes of resistance and yield distributions within the whole MAGIC, and those QTLs were evaluated per se and crossed to a tester (A638) using lattice designs. In each set, a significant single-nucleotide polymorphism (SNP) was considered validated if (1) the same SNP was associated with the trait with a p-value < 0.02, or (2) within a ±2-Mbp interval, an SNP associated with the trait exhibited a p-value < 0.02 and demonstrated linkage disequilibrium (r2 > 0.2) with the SNPs previously reported.Results and discussionThe novel QTL validation approach was implemented using improved experimental designs that led to higher heritability estimates for both traits compared to those estimated with the whole MAGIC population. The procedure used allowed us to jointly validate several QTL and to ascertain their possible contribution to hybrid improvement. Specifically, nearly three-quarters of the QTLs for tunnel length were confirmed. Notably, QTLs located in the genomic region 6.05–6.07 were consistently validated across different sets and have been previously linked to resistance against stalk tunneling in various mapping populations. For grain yield, approximately 10 out of 16 QTLs were validated. The validation rate for yield was lower than for tunnel length, likely due to the influence of dominance and/or epistatic effects. Overall, 9 out of 21 QTLs for tunnel length and 6 out of 17 QTLs for grain yield identified in our previous research were validated across both validation sets, indicating a moderate genetic correlation between per se and testcross performance of inbred lines. These findings offer insights into the reliability of QTL and genomic predictions, both derived from assessments conducted on the entire MAGIC population. Genomic predictions for tunnel length based on inbred line evaluations could be useful to develop more resistant hybrids; meanwhile, genomic prediction for yield could only be valid in a homozygous background.

Investigations on the effect of kaolin catalyst on the yield of various products obtained from pyrolysis of low-density polyethylene (LDPE) wastes and reaction kinetics.

The pyrolysis characteristics of low-density polyethylene (LDPE) wastes were investigated in a batch pyrolyzer in the temperature range of 550-650°C for a varying heating rate (5, 10, 15, 20, and 25°C/min) alone and using kaolin catalyst (10, 15, and 20% w/w). The yield distribution of various products under varying operating conditions was investigated, and the corresponding favourable conditions for maximum yield of pyrolytic oil (PO) were identified. The possible effect of the catalyst on pyrolysis kinetics was explored by extracting activation energies from the TGA experiments. The various model-free iso-conversion methods estimated the activation energy of 162-166kJ/mol during thermal degradation of LDPE, while it shifted to a lower range (138-145kJ/mol) on catalytic treatment. The catalytic pyrolysis with a 20% (by mass) catalyst in a batch pyrolyzer provided the maximum PO yield with the least solid residue at 600°C using a heating rate of 20°C/min, while the temperature of 600°C with 10% catalyst showed the least liquid product with more solid residue. The presence of a catalyst improved the oil quality with transparency and lower viscosity due to lighter hydrocarbons and higher aromatic content. The FTIR analysis detected various organic groups-alkanes, alkenes, cyclic, and aromatics in the oil. GC tests of gaseous products confirmed the gases like hydrogen, propane, butane, and propylene. The present study aims to provide technical know-how for the effective utilization of LDPE wastes towards clean energy sources and create a plastic-free green environment.

Production of Ag nanowires with high yield and narrow size distribution by promoting nucleation through hot injection and their application to disposable paper electrodes

The polyol method has been the most commonly used technique for fabricating silver nanowires (Ag NWs), wherein additive reagents facilitate precise control over their morphology. In this work, we introduced a straightforward approach to synthesize Ag NWs with minimal impurities and uniform diameter by using hot injection method. The underlying mechanism and final product are evaluated, and the scalable process was optimized to achieve a high yield (∼87.8 %). It was found that the hot injection of a Ag precursor at 170 °C significantly accelerated the reduction of Ag ions. The issue of the uniformity of Ag NWs was addressed by adding an organic halide to control the nucleation rate of Ag. The resulting Ag NWs solution was used to impregnated cellulose paper, forming an enhanced electrical network compared to other substrates. Given the low commercial cost of paper, this approach holds significant potential for widespread use in disposable electrodes. Furthermore, after subjecting the fabricated paper electrodes to various forms of damage, the connected LED bulbs consistently maintained their brightness. This demonstrates the network stability and suitability of these electrodes for flexible applications.

Understanding scale effects and differentiation mechanisms of ecosystem services tradeoffs and synergies relationship: A case study of the Lishui River Basin, China

Understanding the scale effect of relationships among ecosystem services (ESs) and the factors influencing these relationships, is vital for sustainable management of ESs. Previous research has focused less on multiple scale units, and the driving mechanisms behind ESs interactions remain unclear. Therefore, in this study, we assessed the spatial distribution of water yield (WY), water purification (WP), and soil conservation (SC) across four spatial scales (3 km × 3 km grid, 5 km × 5 km grid, sub-watershed, and county scales) in the Lishui River Basin of China during 2020. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, geographical weighted regression, and Pearson correlation analysis, we examined tradeoffs and synergies among these ESs at various scales. Additionally, we employed a geographic detector to analyze the reactions and scale effects among these tradeoffs/synergies and social-ecological factors. Our findings revealed the following: (1) Significant spatial heterogeneity in ESs was observed in the basin. The WY decreased from west (2511.65 mm) to east (890.06 mm), with high WP values concentrated in the eastern part, and high SC values (8.30 × 105 t/hm2) found in densely forested and grassland areas surrounding water bodies. Forested and cultivated lands were key contributors to ESs in the basin. (2) Across the four investigated scales, tradeoffs or synergies between the same ESs becoming more pronounced as the scale increased. Notably, most ES relationships displayed their strongest synergies at the county scale, with certain spatial areas demonstrating change in interaction directions. (3) The explanatory power of single- or double-factor interactions increased with scale. ESs interactions were found to be closely linked to climatic factors, as well as topographic features (including slope gradient and elevation), with precipitation exhibiting relatively high explanatory power. The interaction of natural and social factors significantly outweighed the combined interaction of dual social factors. This indicated that socio-ecological factors, especially climate and topography, jointly influenced the intensity and direction of ES tradeoffs/synergies. Scale effects arose from the “peak cutting and valley filling” phenomenon during scale transitions. These insights hold significant value for guiding hierarchical management strategies for regional ESs and informing decisions regarding resource allocation.

Post-Neutron Mass Yield Distribution in the Epi-Cadmium Neutron-Induced Fission of 235U

Cumulative and independent yields of various fission products within the mass ranges of 78 to 117 and 123 to 155 have been measured in the epi-cadmium reactor neutron-induced fission of 235U by using an off-line gamma-ray spectrometric technique. The average neutron energy <En> of the epi-cadmium reactor spectrum is 1.9 MeV. From the cumulative yields, post-neutron mass chain yields were obtained by applying the charge distribution correction. In the mass yield distribution, values of the full-width at tenth-maximum of light and heavy mass wings, average light mass <AL> and heavy mass <AH>, and average number of neutrons <ν> were obtained. The peak-to-valley ratio in the epi-cadmium neutron-induced fission of 235U was obtained for the first time. The present data in the 235U(n,f) reaction were compared with the literature data in the 235U(nth,f) reaction to examine the role of excitation energy on the mass yield distribution parameters and nuclear structure effect. The role of the standard I and standard II asymmetric modes of fission was also discussed.

Waiting Times for Sea Level Variations in the Port of Trieste: A Computational Data-Driven Study

We report here a series of detailed statistical analyses on the sea level variations in the Port of Trieste using one of the largest existing data catalogues that covers more than a century of measurements. We show that the distribution of waiting times, which are defined here akin to econophysics, namely the series of shortest time spans between a given sea level L and the next sea level of at least L + δ in the catalogue, exhibits a distinct scale-free character for small values of δ. For large values of δ, the shape of the distribution depends largely on how one treats the periodic components embedded in the sea level dataset. We show that direct analyses of the raw dataset yield distributions similar to the exponential distribution, while pre-processing the sea level data by means of a local averaging numerical recipe leads to Pareto-Tsallis distributions.

Integrating S1A microwave remote sensing and DSSAT CROPGRO simulation model for groundnut area and yield estimation

This study sought to corroborate microwave remote sensing and simulation models to efficiently delineate groundnut cultivation area and to estimate the yield by integration. Near real-time information on crop acreage and yield estimation is essential for making policy decisions. S1A SAR data were downloaded for entire crop growth period of groundnut during Kharif monsoon seasons (June – October) of 2019 and 2020 and were processed using MAPSCAPE RIICE software to extract groundnut cultivated area in the study districts of Tamil Nadu. Spectral dB curve groundnut generated using multi-date Sentinel 1 A SAR data showed a minimum at sowing, reached a peak at the pod development stage and decreased after that towards maturity. Groundnut area map was generated with a classification accuracy of 85.2 and 84.8 per cent with a kappa coefficient of 0.70, and total groundnut area of 104343 and 116199 ha was mapped during Kharif monsoon season 2019 and 2020, respectively. The mean agreement of 75.01 and 84.94 per cent was observed between DSSAT model simulated LAI and observed LAI at thirty monitoring locations in the study area during Kharif monsoon season 2019 and 2020, respectively, whereas agreement for yield was 82.11 and 83.70 per cent with RMSE of less than 20 per cent. Spatial distribution of groundnut LAI and yield was estimated by assimilating dB from satellite image and from DSSAT model, respectively. The estimated mean spatial LAI was 2.81 and 3.52, whereas mean spatial pod yield was 2124 and 2195 Kg ha−1 during Kharif monsoon season 2019 and 2020, respectively with RMSE of less than 20 per cent and R2 for integrating satellite products and simulation model for spatial estimates during both the year was >0.70, it shows the fitness of products towards increased accuracy of estimation.

Multi-functional RAFT reagent to prepare fluorescent hyper-branched macromolecular probes for the detection of Fe3+ ions

Reversible addition-fragmentation chain transfer (RAFT) polymerization has the advantages of precise control, high yield, and narrow molecular weight distribution. These merits make it an ideal method for the preparation of hyper-branched fluorescent polymers (FL-HBPs). In this work, a multi-functional RAFT reagent (AVCT-NLP) was prepared for the synthesis of monodisperse FL-HBPs. Firstly, dithionate compound (CN1) containing a CC double bond was synthesized by the reaction of 4-nitrophenol with 1-(bromomethyl)-4-vinylbenzene. Subsequently, the NO2 group on CN1 was reduced to NH2, and the −Br on the 4-bromo-1,8-naphthalic anhydride was substituted to emit fluorescence. Finally, the fluorophore was introduced by the reaction of NH2 with the anhydride on the naphthalene ring to obtain AVCT-NLP which contains fluorophore, dithionate, and CC double bond in one molecule. AVCT-NLP was then used to synthesize the homopolymer (PAVCT-NLP) as both monomer and RAFT reagent. Moreover, PAVCT-NLP served as a macromolecular RAFT reagent, and hydrophilic monomer methacrylic acid (MAA) or 3-butene-1,2-diol were polymerized to obtain amphiphilic hyper-branched polymers (FL-AHBP-1, FL-AHBP-2). All the as-prepared macromolecules (PAVCT-NLP, FL-AHBP-1, and FL-AHBP-2) exhibited specific recognition and high sensitivity for Fe3+. The fluorescence quenching reached up to 74.4 %, with the limit of detection (LOD) as low as 1.82 nM. Additionally, the probe demonstrated pH stability and environmental adaptability. Due to the multi-functional structures of AVCT-NLP, it could be subjected to RAFT polymerization to obtain a monodisperse hyper-branched polymer. All polymers have a high fluorophore density and the polydispersity index (PDI) of the polymer is close to 1.00. This paper provided an effective approach for synthesizing polymer fluorescent probes with versatile structure and fluorescence properties in future applications.

Producer Welfare Benefits of Rating Area Yield Crop Insurance

Index-based insurance is an innovative concept for evaluating agricultural risks and payouts, which uses an index instead of traditional on-site loss assessment. Area yield insurance, as an index-based approach, is an effective strategy to mitigate moral hazard and adverse selection issues. This study aims to develop area yield insurance as a new insurance plan in Iran for two major crops: wheat and barley. It utilized kernel and joint kernel distributions to price the insurance and assessed producer welfare benefits by comparing the certainty equivalence (CE) of farmers’ utility with and without the policy. Data were collected from East Azerbaijan Province, including county-level yield data for irrigated and rainfed wheat and barley from 1975 to 2019 and 446 individual-level yield data from 2015 to 2019. A two-stage method was used to model yield risk: the first stage fits a trend model, while the second estimates the yield distributions with the detrended data. The results showed a significant difference in premiums calculated by the two distributions, with joint kernel distribution offering the best empirical fit and reasonable premiums. The findings indicate that area yield crop insurance provides positive welfare benefits and should serve as a viable alternative or complement to existing yield insurance plans. The successful implementation of this policy in various countries suggests it can be a suitable risk management program for developing countries like Iran.

Cu promoted Ni-Ca dual functional materials for integrated CO2 capture and utilization in O2-containing flue gas: Eliminating the delay in the utilization stage

Integrating CO2 capture and utilization by dry reforming methane (ICCU-DRM) technology is promising in concentrating and utilizing diluted CO2 from flue gas, which depends on developing Dual Functional Materials (DFMs) with adsorption and catalytic sites. Ni-Ca materials are widely applied in ICCU-DRM due to their excellent catalytic and CO2 adsorption properties. Nevertheless, the challenge remains that oxygen in the actual flue gas would oxidize the active Ni, leading to a delay in the DRM stage. In this work, Cu-promoted, ZrO2-stabilized Ni-Ca based DFMs were developed, which suppressed the delay and enhanced the performance of the DRM reaction. Attribute to the excellent reducing properties, Cu could be rapidly reduced in the CH4 atmosphere and further promote the reduction of NiO to the catalytically active Ni monomers by activating methane to generate reactive hydrogen (H*). What’s more, attributed to the disappearance of delay times, it is accompanied by a satisfying yield distribution with a 0.11 decrease in H2/CO ratio and an improvement in conversion, especially CH4 conversion with an increase of more than 10%. In addition, the bifunctional material also exhibits favorable cycling stability due to the stabilizers.

Exploring Biomass Conversion Technologies: From Raw Materials to Valuable Products

The global demand for eco-friendly energy has propelled biomass into the spotlight. This report delves into biomass conversion technologies, including biochemical and thermochemical processes, with a focus on hydrothermal conversion. It highlights challenges related to cost-effectiveness and commercial viability. Thermochemical conversion processes, such as pyrolysis and combustion, unlock energy from organic matter. Hydrothermal processing's three approaches and their efficiency are discussed, particularly in biofuels, chemicals, and biochar production. The review analyzes hydrothermal gasification, emphasizing its efficiency and minimal processing time. Carbon and hydrogen gasification efficiencies are crucial in determining gas yields in supercritical conditions. Yield distribution and the influence of feedstock nature and composition on product yield are examined. In conclusion, this report offers insights into biomass conversion technologies and their sustainability for energy and chemical needs.

Improving the productivity of intercropping through above and below ground separation: A case study on photosynthetic characteristics and root distribution

Soybean and maize have important industrial applications, as their yield and production distribution plays an important role in supporting the global agricultural industries. In maize and soybean intercropping system, yield optimization relies on a comprehensive understanding of the complex interactions between above-ground and below-ground processes. Therefore, to investigate the impacts of various planting and separation methods on the spatial distribution of roots, soil moisture, nutrient uptake, photosynthetic physiology of leaves at different positions as well as yield advantages in soybean and maize intercropping, a two-year field experiment was conducted. The results indicted that maize root length density (RLD) increased by 24.3 %-25.5 % in non-separated maize rows and by 25.5 %-30.2 % in 10 cm from the maize rows as compared to the below-ground separated. While soybean RLD was reduced by 13.2–16.5 % in soybean rows and by 34.5 %-36.5 % in 10 cm from soybean rows. The change of the above parameters resulted in a noteworthy decrease in soil water content within the rows of maize, which was effectively mitigated by the below-ground separation. The photosynthetic rate (Pn) in middle and lower leaves of maize increased by 51.1 % and 39.2 % in above and below-ground separation, while less in upper leaves. Above-ground separation can alleviate the decrease of Pn in the upper and middle leaves of soybean, which was only decreased by 2.7 % and 3.0 % as compared with monocropping. The main constraint on maize production is the Pn of the middle and lower leaves, the upper leaves being secondary factors. Intercropping enhanced chlorophyll fluorescence parameters, while below-ground separation decreased it in the middle and lower leaves of maize. However, above-ground separation increased fluorescence characteristics in soybeans across all leaves. Intercropped maize benefited more from the below-ground than above-ground advantages, while the opposite was true for soybeans. The above-ground separation improves the overall LER and contributes the most to the yield advantages.

Efficient synthesis of durene from syngas utilizing composite catalyst of Zr/Al activity-regulated CuZn oxides and alkali-treated HZSM-5-PEG

The one-step syngas conversion process for high-value aromatics represents a promising avenue for transforming non-petroleum resources into valuable chemicals. The utilization of Cu-based catalyst coupled with HZSM-5 enables a highly selective conversion of 1,2,4,5-tetramethylbenzene (durene). The adjustment of structure promoter Zr-Al in CuZn oxide impaired excessive hydrogenation, which solved the temperature mismatch of methanol intermediate synthesis on metal oxides and the aromatization on HZSM-5 zeolites. Multiple characterizations such as N2 adsorption and desorption, XRD, TPR/TPD, SEM-(EDS) proved that high proportion doping of zirconium greatly affected the crystal structure and morphology, which passivated the interaction of CuO/Cu with hydrogen. In addition, Polyethylene glycol modified HZSM-5 with more inter-crystalline mesopores had better diffusion performance to obtain heavy aromatics and prevent the accumulation of carbon deposits. Alkali treatment modulated the ratio of Brønsted and Lewis acids, which was favorable to specific selective alkylation for durene. The relationship between methanol space time yield and aromatic distribution was confirmed and the preferred CuZnZrAl/HZSM-5-PEG obtained CO conversion of 74.4 % with 40.6 % 1,2,4,5-tetramethylbenzene selectivity at 320 ℃ and 4.0 MPa.

Study of Genetic Variability in F2 Population for Yield Enhancing Traits under Direct Seed Condition in Rice (Oryza sativa L.)

The present investigation was conducted to assess the variability, frequency distribution of grain yield and yield attributing traits in F2 generation of DRR Dhan 60 × Pusa 44 (NIL). In order to better understand the genetics of DSR and develop rice genotypes that are aerobic conditions-suited, genetic diversity is crucial. The experimental material consists of 236 F2 plants along with parents which were planted in DSR plot at ICAR-IIRR, Hyderabad. Data recorded for the traits viz., Days to 50% flowering, Plant height, Number of tillers per plant, Flag leaf length, Flag leaf width, Number of panicles per plant, Panicle length, Number of spikelets per panicle, Number of filled grains per panicle, Number of chaffy grains per panicle, Spikelet fertility, Spikelet sterility, 1000-grain weight and Grain yield per plant. The traits, number of productive tillers per plant, number of tillers per plant, flag leaf length and grain yield per plant have high genotypic and phenotypic coefficient of variation. For productive tillers per plant, number of tillers per plant, flag leaf length, and grain yield per plant, high GCV and PCV along with high heritability and high genetic advance as a percentage of mean were noted. This suggests an additive type of gene action and that selection for these traits is effective. These findings indicate that, in aerobic conditions, there was adequate genetic diversity for most of the characters under investigation.

Catalytic pyrolysis of pine needles: Role of zeolite structure and SiO2/Al2O3 ratio on bio‐oil yield and product distribution

AbstractRenewable and sustainable energy production has gained significant attention to meet sustainable development goals (SDGs). Pine needles, an abundant typical forestry residue, can be used as a renewable biomass source for sustainable energy production. Pyrolysis is a well‐established and commercialized technique for the thermochemical valorization of lignocellulosic biomass. The present work focuses on improving the bio‐oil yield by introducing SiO2‐Al2O3‐based catalysts, including different zeolites and SiO2‐Al2O3 materials with varying SiO2‐Al2O3 ratios, during the pyrolysis. Bio‐oil yield increased from 45.2 wt.% to 47.2 wt.% with the introduction of SiO2‐Al2O3 catalysts and increased to 51.2 wt.% and 50.6 wt.% with HZSM‐5 and Y‐zeolite, respectively, and decreased to 40.0 wt.% with β‐zeolite catalyst. The pyrolysis experiments of physically mixed biomass and catalyst were carried out in a fixed‐bed down‐flow reactor. Various process parameters such as temperature, retention time, and catalyst‐to‐biomass ratio were examined to evaluate their effect on product yield. The catalyst's introduction slightly decreased phenolic compound content, enhancing carbonyl and hydrocarbon production. Maximum improvement in bio‐oil yield by 6 wt.% was achieved using an H‐ZSM‐5 catalyst at 450°C temperature and 30 min residence time with a catalyst‐to‐biomass ratio of 1:4.

Implications of crop yield distributions for multiperil crop insurance rating in Ghana: a lasso model application

PurposeThe objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda.Design/methodology/approachThis study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model.FindingsIt is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields.Research limitations/implicationsThe primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions.Originality/valueThis is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.

Designing iron‐ethyl cellulose microparticles to prevent unwanted color changes during iron fortification of milk tea

AbstractIron deficiency affects an estimated 1.62 billion individuals worldwide, while Asia and Africa bearing the highest burden. The widespread consumption, unique sensory properties and cultural significance of tea make it an appealing avenue for iron fortification. Obtaining microparticles for food fortification with acceptable organoleptic properties is key for consumer acceptability. Microcapsules were prepared with Aquacoat and various iron salts. The experiments were designed to understand the effect of formulation variables, i.e. type of iron salt, ratio of iron‐to‐coating, temperature and flow rate of the process. The iron compound significantly impacted yield, particle formation, and size distribution (5‐15 μm). Post treatment by curing at specified relative humidity and temperature improved the colour in milk tea with ΔE reduced from 7 to 2 in NBS units. Microparticles from FeCl3 exhibited superior morphology and colour‐masking efficacy, inhibiting iron‐polyphenol interaction in tea and show promise as iron fortificants for milky black tea.Practical applicationsMicroencapsulation is a highly effective technique for encapsulating active iron cores within inert coating materials, ensuring the desired chemical and physical properties. Using spray drying, we can produce small and uniformly sized particles ranging from 1 to 20 μm. This can easily be utilized for fortification of beverage like hot tea/coffee or similar products. The success of the current process is evaluated based on several key factors, including process yield, encapsulation efficiency, and sensory properties of fortified tea.

Modelling of the above-ground biomass and ecological composition of semi-natural grasslands on the strenght of remote sensing data and machine learning algorithms

Semi-natural grasslands represent a vital source of forage and fodder for livestock farming, fulfilling an essential ecological function.This study upholded the efficacy of Sentinel-2 time series data in conjunction with ground data on aboveground biomass (AGB), ecological composition, and floristic composition for the development of a prediction model to determine AGB yield and species distribution in a semi-natural grassland. Two machine learning (ML) approaches, namely K-means clustering, and Random Forest (RF) classification were employed in a semi-natural grassland in Southern Europe. The study was subjected to a qualitative ground survey that identified two main groups. The first group (Cluster 1) was composed of nitrophilous species or nitrophilous species combined with xerophilous species and some hygrophilous species.The second group, on the other hand, consisted of only hygrophilous species or hygrophilous species associated with Ranunculus acris, Bromus hordeaceus, Brassica nigra, Lolium perenne and Trifolium sp. (Cluster 2).Using the above-mentioned algorithms, the study identified thetwo distinctive areas based on AGB estimates in the period beginning in March (vegetative growth) and extending to the first half of May. The R2 value for K-means classification was 0.59, with a root mean square error (RMSE) value of 1 t ha−1.The R2 value for the RF classification was 0.69, with an RMSE value of 0.59 t ha−1. The integration of ground-based data and ML classification techniques enables the generation of accurate, dynamic information on grassland, core attributes that play an integral role in sustainable ecosystem management and conservation.

Mid-season nitrogen management for winter wheat under price and weather uncertainty

ContextIn-season nitrogen (N) management tools are essential for optimizing N application rates, maximizing farmers’ economic returns and minimizing adverse environmental impacts. The primary limitation to developing such tools is the risk associated with uncertainties in weather forecasts and crop price projections required to estimate yields and returns for different N rates. Therefore, characterizing the risk associated with these uncertainties is crucial for determining optimum N rates in-season. ObjectiveThis study investigated the N application decision-making process for farmers, accounting for risks associated with weather and crop price uncertainties through crop modeling and economic analysis. MethodsWe used field trial data for winter wheat in Kansas to examine how optimal nitrogen rates and economic returns vary over sites, years, and differing farmers’ risk attitudes. First, the Environmental Policy Integrated Climate (EPIC) agroecosystem model was used to simulate the distribution of final yields under different N applications during early spring. Then, an autoregressive moving average (ARMA) model estimated the wheat price distribution at harvest based on historical prices. Finally, optimal N application rates for farmers with different risk appetites were estimated using two risk decision models: the constant-absolute-risk-averse (CARA) expected utility model, which treats upside (higher-than-expected returns) and downside (lower-than-expected returns) deviations equally, and the invariant-preference, generalized-deviation (IPGD) model, which focuses on downside risk. ResultsWe found that optimal N rates vary greatly between sites and years, as well as across farmers with different risk preferences. Due to the positive skewness of economic return distribution, farmers tend to apply lower N rates when considering downside risk. On average, the optimal N rate for farmers with a CARA coefficient of 0.002 is 77 kg/ha in the CARA model and 67 kg/ha in the IPGD model. Compared to the outcome of risk-neutral N usage, risk-averse N usage for a farmer with a CARA coefficient of 0.008 could reduce the uncertainty (standard deviation) of return by 6.2 %, on average, while the expected return decreased by only 1.2 %. ConclusionsBy lowering the N rate, risk-averse farmers would reduce the uncertainty of returns and incur a minor return loss, suggesting the possibility of improving agricultural resilience while also improving N use efficiency. Our analysis also underscores the importance of yearly site-specific N management, given the substantial variation in optimal rates across years and locations. SignificanceThis study provides the foundation for an N application decision framework that considers both weather and price uncertainty. The analysis also demonstrates the potential co-benefit of enhancing agriculture’s climate and market resilience while potentially lowering N losses.