Yield Measurements Research Articles

Accuracy of agricultural data and implications for policy: Evidence from maize farmer recall surveys and crop cuts in the Guinea Savannah zone of Ghana

CONTEXTThe need for accurate data in policy design aimed at agricultural transformation cannot be overemphasized. Unfortunately, the relevance of agricultural research in addressing the needs of farmers has been questioned due to debates about appropriate methodologies and approaches for establishing research activities and, in other instances, poorly reasoned premises and paltry delineation, definition, and understanding of the system being studied. For a country like Ghana, where agricultural transformation is a prerequisite for its sustainable development, an understanding of the accuracy of farm data measurement is necessary. OBJECTIVEThe objective of this study is to estimate the yield measurement error and to analyze the sources of such measurement errors among the farmers of the Guinea Savannah zone of Ghana. METHODSTwo years' data for both farmer recall surveys and crop cuts were used. Descriptive statistics, regression and sensitivity analyses were done to achieve the objectives of the study. RESULTS AND CONCLUSIONSOn average, farmers' recall of maize yields (1544.6 kg/ha) was lower than the estimated crop cut yields (2593.9 kg/ha), although about 11.2% of the farmers recalled higher yields than their estimated crop cut yields. The estimated average percentage error in yield measurement between crop cuts and farmer surveys was 36.4%. These yield measurement errors are due to systematic biases, including those involving the recall of farm size, and the socioeconomic conditions of the farmers. Although a crop cut is costly, it has limited bias in providing a better measure of yield than farmer recall surveys. Irrespective of the method used, however, more attention should be given to potential sources of systematic bias in the design and data collection. Moreover, for proper interpretation, yield estimates from recall surveys and crop cuts should be properly interpreted as economic yield and biological yields, respectively. SIGNIFICANCEThis paper provided clarity on the differences in maize yield estimates in Ghana and provided measures on how to improve precision in yield data.

Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors

The ability to detect and respond to vineyard spatial variation can lead to improved management—a practice known as precision viticulture. The goal of this study was to determine if remote sensors can enhance precision viticulture applications by detecting vineyard spatial variation. The hypothesis was that differences in vine spectral reflectance, as detected by remote sensors, would be associated with variations in viticultural variables due to known relationships with vine size, structure, and pigmentation. Riesling grapevines were geolocated within six commercial vineyards across Niagara, Ontario. Water status, vine size, winter hardiness, virus titer, yield components, and berry composition were measured on these vines. Remote sensing technologies subsequently collected multispectral data by unmanned aerial vehicles and by proximal sensing technology (GreenSeeker™), which were transformed into the Normalized Difference Vegetation Index (NDVI). Direct relationships between NDVI and vine size, water status, yield, berry weight, and titratable acidity were observed, as well as inverse relationships between NDVI and Brix and potentially volatile terpenes. Remote sensing demonstrated the ability to detect vineyard areas differing in measures of vine health, yield, and berry composition in certain sites and years; however, more research is needed to determine when these technologies should be used for precision viticulture applications.

Comparison of the effects of calcium ammonium nitrate and stabilized urea fertilizers on grass and silage yields and quality

AbstractEffects of Calcium Ammonium Nitrate (CAN) and Stabilized Urea (SU) fertilizers were compared for herbage yield and composition and silage composition of perennial ryegrass (Lolium perenne L.) swards over a two‐year study. The aim of this experiment was to establish the likely impacts of farmers transitioning to using SU fertilizers in order to reduce the nitrogenous emissions associated with silage production as the agriculture industry moves to address the challenges of climate change. Experimental plots of 7.5 m2 area were established in a randomized block experiment comprising 4 replicates of 18 treatments in a 3 × 6 factorial design (4 × 3 × 6 = 72 plots) for each of 3 silage harvest periods in 2018, and repeated in 2019. Herbage samples and yield measurements were collected weekly for weeks 2–7 post fertilizer application, and herbage harvested after seven weeks regrowth was ensiled and silage was analysed after 100 days. There were no significance differences overall between responses of CAN and SU fertilized plots in terms of grass dry matter (DM) yield and quality parameters (acid detergent fibre (ADF), ash, buffering capacity (BC), metabolisable Energy (ME), nitrate (NO32−), nitrogen (N), or water soluble carbohydrate (WSC) or silage quality parameters (DM, volatile corrected organic dry matter (VCODM), ammonia nitrogen as a fraction of total nitrogen (NH3‐N/ Total N), pH, crude protein (CP), lactic acid (LA), acetic acid (AA), propionic acid (PA), butyric acid (BA), ethanol, propanol, ADF, ash, WSC or dry matter digestibility (DMD). Numerous significant differences between week and between cut were identified for grass quality, silage quality and grass ensilability parameters, and in the grass DM yields recorded. However, in many instances there was no associated treatment effect identified. There were no significant interactions between CAN and SU treatments and either cut, or week for nitrate concentration or yield. Variance of nitrate concentration was highest at the third cut and indicates a reduction in the nitrogen use efficiency of the 3rd cut silage harvest, potentially demonstrating a need to reduce fertilizer application recommendations for late‐season silage harvests. Overall, the results of this study support the hypothesis that replacing the currently favoured CAN fertilizer products with SU will not impact production of grass silage in Northern Ireland, meaning SU can be confidently adopted by farmers as a mitigation strategy to reduce farm nitrogenous emissions without reducing levels of sward productivity. This study recorded average annual grass silage DM yields of 15.5 and 16.1 t DM/ha from the CAN and SU fertilizer treatments respectively, which were not significantly different. In addition, the yield gain response to the application of each fertilizer type were not significantly different at 21.51 and 23.24 kg of DM produced for every kg of N applied as SU or CAN respectively, relative to control plots receiving 0 nitrogen.

Bayesian inference for parameter identification in mechanistic models, exemplified using a cow lifetime performance model

Mechanistic models are valuable tools for studying the underlying mechanisms of complex biological phenomena. For example, cow lifespan models can be used to identify differences in resource acquisition and allocation strategies between individuals, which is relevant for decision-making in breeding programs. In such models, differences in simulated traits between individuals are consequences of the parameter set that represents the genetic potential of each animal and its interaction with the environment. This indicates that the identification of these differences is essentially a search for individual parameters. In mechanistic models, this search is generally a non-convex problem that has different local minima because the parameters interact within these models. Due to this and to the simulation time length (e.g. years), there is uncertainty associated with the inference of the parameter values for each individual. This uncertainty can be quantified using Bayesian inference since this approach treats the model parameters as random variables with an underlying probability distribution that describes them. The objective of this work was to employ the Delayed Rejection Adaptive Metropolis (DRAM) algorithm to identify the parameters of a cows’ lifespan model using two datasets of Holstein cows. The datasets contain periodic measurements of Milk Yield (MY), BW, and Body Condition Score (BCS). Additionally, one of the two datasets has information of BW from birth to first calving. The average Mean Absolute Percentage Error (MAPE) minimisation between the simulated and experimental data (MY, BW and BCS) was used as the objective function for parameter search. The Bayesian inference performance was compared with four optimisation metaheuristic approaches: Differential Evolution, Genetic Algorithm, Particle Swarm Optimisation, and Simulated Annealing. Although the results show that all methods are efficient in finding parameter values that reduce the distance between the simulated and experimental data (MAPE < 10%), the DRAM method is more efficient in terms of computational cost, and the parameter distributions obtained with this method offer more information about the statistical properties of each parameter (e.g. median).

Site Factors Related to Dry Farm Vegetable Productivity and Quality in the Willamette Valley of Oregon

Dry farming has been defined as rainfed crop production in a climate with more than 20 inches of annual precipitation, but where most precipitation falls outside the growing season. Dry farming is garnering interest in the western United States because it allows farmers to produce crops despite a lack of access to irrigation or water rights or to eliminate the infrastructure, labor, and energy costs of irrigation systems. Sites have differing suitability for dry farming, and some sites that can be farmed with irrigation will perform poorly when dry-farmed. To determine site factors associated with dry farm yield and fruit quality, trials of ‘Early Girl’ tomato (Solanum lycopersicum) and ‘North Georgia Candy Roaster’ winter squash (Cucurbita maxima) were conducted at 17 participant farms in the Willamette Valley in Oregon, USA, in 2018 and 2019. The mean blossom-end rot (BER) incidence was higher in the Willamette Valley than in coastal California; this was probably because of the Willamette Valley’s hotter and drier climate. Increasing the available water-holding capacity of soil, total available water (available water-holding capacity of the soil plus in-season rainfall), native productivity rating, soil pH (0–6 inches and 24–36 inches), soil nutrient concentrations (0–6 inches and 24–36 inches), and in-season rainfall were positively associated with at least one measure of tomato or winter squash yield, fruit number, or average fruit weight. An earlier planting date was positively associated with winter squash total yield and total fruit number in 2019. The water-limited yield potential (the total yield potential if water was the only limiting factor) for 20-ft2/plant plots was estimated to be 2.2 tons/acre per inch for tomato and 2.8 tons/acre per inch for winter squash. In 2019, high-density plantings (20 ft2/plant) had higher tomato and winter squash mean total yields, mean total fruit numbers, and mean tomato unblemished yield than low-density plantings (40 ft2/plant). In 2019, planting tomato at 20 ft2/plant decreased the mean BER incidence by 15.6% when compared with planting tomato at 40 ft2/plant.

Modelling adaptation measures to improve maize production and reduce soil N2O emissions under climate change in Northeast China

Optimizing management practices is essential to guarantee crop yield and reduce greenhouse gas emissions for the sustainable agricultural production under climate change. The objectives of this study were to i) calibrate and evaluate the Denitrification-Decomposition (DNDC) model using the measurements of maize yield, nitrogen (N) uptake, soil temperature, soil moisture, soil inorganic N and nitrous oxide (N2O) emissions from a 5-year maize field experiment in Northeast China (NEC) and ii) simulate the impacts of climate change on maize yield and N2O emissions and explore adaptation strategies based on DNDC modelling. For N-fertilized treatments (farmers’ practice, FP-N; current optimized management, OPT-N), the DNDC model overall showed “good” performance in simulating maize yield, aboveground biomass, N uptake and soil inorganic N based on the average statistical values. The model also performed well in simulating soil moisture, soil temperature and N2O emissions. For N-unfertilized treatments, however, the DNDC model overestimated crop yield and N uptake mainly due to the overestimation of crop growth under low-temperature stress conditions, and the underestimation of soil inorganic N resulted from the underestimated N mineralization and N availability from the dissolved inorganic N pools under continuous N deficiency. The DNDC model showed that compared to the baseline scenario, future climate change would improve the maize yield (1.4–8.8%) while increasing the N2O emissions (37.6–83.4%) without adaptation measures in NEC. Compared to OPT-N management, solely adjusting N fertilization rate (200–220 kg N ha−1) would concurrently increase maize yield (5.0–8.7%) and N2O emissions (0.9–18.6%), while the adoption of optimized planting dates (April 27th to May 18th), new cultivars (with thermal degree days ranging from 2550 to 2700 °C), and the implementation of no-tillage and maize-soybean rotation would increase the maize yield by 1.8–8.9% and reduce the N2O emissions by 0.7–26.5% under future climate change scenarios. This study provided insights into the adaptive management strategies for the sustainable maize production under climate change in NEC.

Characterization of the scintillation response of water-based liquid scintillator to alpha particles, and implications for particle identification

Next-generation large-scale neutrino detectors, from Eos, at the 1 t scale, to Theia, at the 10 s-of-kt scale, will utilize differences in both the scintillation and Cherenkov light emission for different particle species to perform background rejection. This manuscript presents measurements of the scintillation light yield and emission time profile of water-based liquid scintillator samples in response to α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} radiation. These measurements are used as input to simulation models used to make predictions for future detectors. In particular, we present the timing-based particle identification achievable in generic water-based scintillator detectors at the 4 t, 1 kt, and 100 kt scales. We find that α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document}/β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document} discrimination improves with increasing scintillation concentration and we identify better than 80% α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} rejection for 90% β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document} acceptance in 10% water-based liquid scintillator, at the 4 t scale.

Effect of Elevated Air Temperature on the Growth and Yield of Paddy Rice

Rice is one of the major food crops, particularly in Asia. However, it is vulnerable to high temperature and has high yield fluctuations. Monitoring crop growth and physiological responses to high temperatures can help us better understand the agricultural impacts of global warming. The aim of this study is to monitor growth, development, and physiological responses to high temperature conditions on paddy rice and to assess their combined effects on yield. In this study, changes to growth, maturity, and senescence in paddy rice throughout the growing season were identified under elevated air temperature conditions created by a temperature gradient field chamber (TGFC). That facility provides a gradient from the ambient air temperature (AT) to 3 °C above AT (AT + 3 °C). To represent crop physiology and productivity, we measured the plant height, chlorophyll, normalized difference vegetation index (NDVI), and maximum photosynthetic rate (Amax) to assess growth and physiological processes, and heat stress effects on four yield measurements were assessed using the heating degree day index. Rice height increased more rapidly in the AT + 3 °C treatment from the early growth stage to heading, while SPAD and NDVI decreased more rapidly at AT after heading. The Amax of AT and AT + 3 °C was not significantly different in the tillering stage. However, it was higher at AT in the booting stage but higher at AT + 3 °C in the grain filling stage. These results indicate that paddy rice was not affected by heat stress at the tillering stage, but a cumulative effect emerged by the booting stage. Further, photosynthetic capacity was maintained much later into the grain filling stage at AT + 3 °C. These results will be useful for understanding the growth and physiological responses of paddy rice to global warming.

Striking Change in Radiative Lifetime and Asymmetry Ratio with Temperature for the Double Perovskite Cs2NaEuCl6

AbstractThe measurement of quantum yield (QY) is an important process for a phosphor. This is often accomplished by the comparison of the low‐temperature lifetime with the room temperature (RT) lifetime. It is shown that this procedure provides a considerable underestimation of QY in certain cases. Additionally, the use of the europium ion as an indicator of changes in site symmetry with temperature has severe limitations. This work reveals that a decrease in measured lifetime with increasing temperature of a system cannot always be associated with a descent in site symmetry and may be due to increasing radiative rate.

Overview of interpretive modelling of fusion performance in JET DTE2 discharges with TRANSP

In the paper we present an overview of interpretive modelling of a database of JET-ILW 2021 D-T discharges using the TRANSP code. The main aim is to assess our capability of computationally reproducing the fusion performance of various D-T plasma scenarios using different external heating and D-T mixtures, and to understand the performance driving mechanisms. We find that interpretive simulations confirm a general power-law relationship between increasing external heating power and fusion output, which is supported by absolutely calibrated neutron yield measurements. A comparison of measured and computed D-T neutron rates shows that the calculations’ discrepancy depends on the absolute neutron yield. The calculations are found to agree well with measurements for higher performing discharges with external heating power above ∼20 , while low-neutron shots display an average discrepancy of around +40% compared to measured neutron yields. A similar trend is found for the ratio between thermal and beam-target fusion, where larger discrepancies are seen in shots with dominant beam-driven performance. We compare the observations to studies of JET-ILW D discharges, to find that on average the fusion performance is well modelled over a range of heating power, although an increased unsystematic deviation for lower-performing shots is observed. The ratio between thermal and beam-induced D-T fusion is found to be increasing weakly with growing external heating power, with a maximum value of 1 achieved in a baseline scenario experiment. An evaluation of the fusion power computational uncertainty shows a strong dependence on the plasma scenario type and fusion drive characteristics, varying between ±25% and 35%. D-T fusion alpha simulations show that the ratio between volume-integrated electron and ion heating from alphas is 10 for the majority of analysed discharges. Alphas are computed to contribute between ∼15% and 40% to the total electron heating in the core of highest performing D-T discharges. An alternative workflow to TRANSP was employed to model JET D-T plasmas with the highest fusion yield and dominant non-thermal fusion component because of the use of fundamental radio-frequency heating of a large minority in the scenario, which is calculated to have provided ∼10% to the total fusion power.

RTAL: An edge computing method for real-time rice lodging assessment

Rice is a globally important crop that plays an important role in feeding more and more of the world's population as we cope with climate change and population growth. Rice lodging is one of the main yield-reducing factors in rice production, which also has a direct impact on rice quality and leads to harvest difficulties, so it is very important to obtain lodging data in time. Lodging assessment is a tedious task that generally requires a lot of time and labor due to the large area of land involved. This study proposes an edge computing method for rice lodging areas suitable for unmanned aerial vehicles (UAVs) without post-processing, called Real Time Rice Lodging Area Calculation Method (RTAL). The RTAL method is based on deep learning and photogrammetry, and can calculate large-scale farmland lodging areas in real time using only edge computing devices. Tested on the edge computing device Nvidia Jetson Xavier NX, when the ground resolution is 0.1 m, the fastest prediction speed can reach 14417.9 m2/s. When the operation time of a single sortie is 80 min, the predicted area can reach 10 km2. This method greatly improves the efficiency of prediction, and can provide real-time help in rice yield measurement or disaster damage assessment.

Evaluation and Improvement of Bio-Based Sustainable Resin Derived from Formic-Acid-Modified Epoxidized Soybean Oil for Packaging Applications.

Bio-based epoxy resin materials have obtained significant attention in the packaging industry due to concerns about the environmental and economic impacts of traditional petroleum-based plastics. The aim of this research is to improve bio-based resins' properties by investigating varying formic acid contents in the presence of a green catalyst and characterizing their physical, chemical, and mechanical properties for further scaled-up bio-based resin production for industrial packaging applications. The crude soybean oil was epoxidized with formic acid as an oxidizing agent at varying equivalent weights of 10:1 to 10:10 of soybean oil: formic acid in the presence of hydrogen peroxide and choline chloride-oxalic acid as a bi-functional green catalyst. The effect of increasing the amount of formic acid used to epoxidize crude soybean oil was evaluated with infrared (IR) spectroscopy, rheological, and epoxy yield measurements. The results demonstrated that formic acid significantly influenced the epoxidation of soybean oil, leading to a higher conversion of carbon-carbon double bonds, with a selectivity of 98% when the ratio of soybean oil to formic acid was between 10:5 and 10:10. The bio-resin film was formulated using the improved epoxidized soybean oils-from ESO (10:2.5) to ESO (10:10)-and equal amounts of acrylic acid. The results showed that resin films led to an improvement in tensile strength (ca. 180 MPa) and thermal stability at 360 °C. Although further research is necessary, this study provides valuable insights for designing an effective epoxidation process for renewable sources and developing bio-resin materials for future packaging applications.

PSV-2 Impact of Diseases in Pig Production on Carcass and Meat Quality

Abstract Several diseases, including Porcine Respiratory and Reproductive Syndrome (PRRS), are present in pig herds. The effects of diseases on growth performance are generally well known, but there is a lack of information on the consequences on carcass and meat quality. The aim of this project was to assess the impact of diseases present in the nursery and finishing on performances, carcass yields and meat quality. The trial was carried out at the Deschambault Swine Evaluation Station, where a project aimed at evaluating disease resilience is being carried out. A total of 480 castrated piglets from 8 different batches entered the nursery barn every 3 weeks. Pigs were observed daily, and a careful examination of the clinical signs was carried out weekly on each animal, to separate pigs showing signs of disease from those in good health. Ultrasound measurements of back fat thickness, muscle depth and intramuscular fat (IMF) level were performed three different times during growth. When the pigs reached a body weight of approximately 120 kg, carcass quality and meat yield measurements were taken 18 to 24 hours after slaughter. The ultimate pH, color, water loss and marbling were measured on the loin. The presence of PRRS and other diseases had a significant impact on the growth performance of pigs in fattening and demonstrates that the presence of the virus in the herd significantly decreases the average daily gain (ADG). This difference has a direct impact on the number of days in fattening. Results for back fat thickness at the end of fattening show significant differences (P &amp;lt; 0.05) between uninfected pigs (14.7 mm), pigs infected in nursery (13.9 mm) and fattening (12.8 mm) only, and pigs that have been ill both in nursery and fattening (12.4 mm). These results indicate that the presence of diseases leads to a decrease in fat deposition in pigs at the end of fattening. However, results for IMF measured at the end of fattening demonstrate that the presence of diseases had no significant impact on the final IMF level (P &amp;gt; 0.10), varying between 2.05% for healthy pigs and 2.13% for animals that have been sick in nursery and fattening. The results obtained in the slaughterhouse have shown that the presence of diseases does not seem to have an impact on the various measures of meat quality (P &amp;gt; 0.05). Only drip loss shows a significant difference (P &amp;lt; 0.05) between healthy and sick animals. However, the low number of sick pigs in the batches evaluated could have limited the observed effects. The work carried out by the CDPQ has made it possible to quantify the impact of diseases by providing a better understanding of their impact on product quality.

Collection, Thermal Treatment, and Remelting End-of-Life Al Packaging in Norway

Aluminum (Al) is used in packaging due to its density, strength, and preservative qualities. The Norwegian packaging flows in 2020 are shown. The paper outlines a laboratory methodology to evaluate the recycling performance of Al packaging where parameters of collection type and pre-treatment are alternated with measurement of yield and analysis of metal quality. Further, the paper evaluates the recyclability of the Al from (1) sorted at a materials recovery facility for municipal waste, (2) from a co-mingled glass and metal packaging collection system, (3) used beverage cans from a deposit system, and (4) from a novel collection scheme. The benefit of pre-treatment is evaluated for all these systems. The results indicate acceptable yield and metal quality from remelting in all four collections systems, but care should be taken in the comparison. Collection, pre-treatment, and melting processes need to be assessed together. Overall, thermal treatment at 550°C is better than at 300°C, which is also worse than just drying at 110°C. This study aims to contribute to assessing Al packaging recycling in Norway and help future developments.

An Effective and Efficient Method to Estimate Average Paddy Yield in Sri Lanka Overcoming the Limitations of the Current Method

The Department of Census and Statistics of Sri Lanka carries out an island-wide scheme of estimating paddy's average yield, called the Crop Cutting Survey. It was debated and argued about the accuracy of the data generated through a cumbersome approach which consumes a lot of labour and resources. The objective of this study was to develop an efficient method that would help overcome the limitations of the current method. This study was accomplished through a questionnaire survey and an experimental survey. The GIS technology has been applied to measure the cultivated area and yield measurements by weighting the harvested yield. The weight measurements were subjected to different scaling factors derived through the study. The sample survey of the new methodology was carried out in the Ampara district for 2 paddy varieties namely BG357 and AT362 for the 2017/2018 Maha season. A stratified Random sampling method was used for the selection of paddy parcels. Average paddy yield, scaling factors, and cost of production of these two paddy varieties were calculated separately. The district's estimated paddy yield through the proposed method is 4568.17 kg/ ha, which is not significantly different from the published average (4562 kg ha-1) in paddy statistics of 2017/2018. This is obvious that the proposed method was more effective and efficient than the current method, which assists the government in planning and making policy decisions at the correct time.

Smaller-the-better-type six sigma product index

Based on some studies, there are many important parts of tool machines, all of which have some essential smaller-the-better-type quality characteristics. The six sigma quality index of the smaller-the-better type offers accurate measurement of the process yield and the six sigma quality level. In this paper, we first proposed a six sigma product index by integrating all evaluation indicators for products that contain several quality characteristics of the smaller-the-better type. Next, we derived the confidence interval of this six sigma product index and developed an evaluation model for product quality. When a product passes the evaluation of this model, not only can it be guaranteed that the product reaches the required quality level, but also a high rate of product yield can be ensured. In addition, we also created a product improvement testing model, which can avoid missing opportunities for improvement in the process to ensure improvement effects. This complete evaluation and improvement model is applicable to the entire machine tool industry chain. It can not only increase the product value of the machine tool industry chain but also decrease environmental pollution caused by rework or scrap, which is beneficial to companies to enhance their image of fulfilling social responsibilities. Apart from the above advantages, the model formed in this paper is based on confidence intervals, thereby reducing the chance of misjudgment resulting from sampling error.

Absolute quantitation of peptides and proteins by coulometric mass spectrometry after derivatization

Peptide/protein quantitation using mass spectrometry (MS) is advantageous due to its high sensitivity. Traditional absolute peptide quantitation methods rely on making calibration curves using peptide standards or isotope-labelled peptide standards, which are expensive and take time to synthesize. A method which can eliminate the need for using standards would be beneficial. Recently, we developed coulometric mass spectrometry (CMS) which can be used to quantify peptides that are oxidizable (e.g., those containing tyrosine or tryptophan), without using peptide standard. The method is based on electrochemical oxidation of peptides followed by MS measurement of the oxidation yield. However, it cannot be directly used to quantify peptides without oxidizable residues. To extend this method for quantifying peptides/proteins in general, in this study, we adopted a derivatization strategy, in which a target peptide is first tagged with an electroactive reagent such as monocarboxymethylene blue NHS ester (MCMB-NHS ester), followed with quantitation by CMS. To illustrate the power of this method, we have analyzed peptides MG and RPPGFSPFR. The quantification error was less than 5%. Using RPPGFSPFR as an example, the quantitation sensitivity of the technique was found to be 0.25 pmol. Furthermore, we also used the strategy to quantify proteins cytochrome C and β-casein with an error of 2–26 %.

Effect of adding Biohealth, zeolite and mineral fertilizer on some measurements of potato yield

To know the effect of bio-enhancer (zeolite), biohealth, mineral fertilizers and their interactions, the possibility of replacing mineral fertilizers with bio-enhancers and bio-enhancers, and their effect on some potato yield measurements. A field experiment was conducted at one of the field stations of the College of Agricultural Engineering Sciences, University of Baghdad, near the electronic calculator center, research station (F) in Al-Jadriya region in the loam mixture soil during autumn season 2021-2022 AD, It was designed using a completely randomized block design (RCBD) with three replicates. The factors of the study experiment included three levels of zeolite (0, 6 tons ha-1, and 12 tons ha-1), which were symbolized by (Z0), (Z1) and (Z2), respectively. As for the bio-enhancer (Biohealth), it was added at two levels (0 and 5) to kg ha-1, which was symbolized by the symbols (B0) and (B1), respectively. As for the mineral fertilizer treatments, they were added at three levels (0, 50%, and 75%) of the fertilizer recommendation, which was (300 kg ha-1 N, 100 kg ha-1 P, and 300 kg ha-1 potassium), symbolized by (F0). and (F1) and (F2), respectively. Potato seed, Rivera cultivar, was planted as a furrow on 1/23/2022. The area of the experimental unit was 6 m 2 (3 m 2 x 2 m2 ). Eighteen treatments were distributed randomly in the sectors (replicates), so the number of units became 54 experimental units. Keywords: Potato; Biohealth; Zeolite; Mineral Fertilizer.

Water Stress Alters Physiological, Spectral, and Agronomic Indexes of Wheat Genotypes.

Selecting drought-tolerant and more water-efficient wheat genotypes is a research priority, specifically in regions with irregular rainfall or areas where climate change is expected to result in reduced water availability. The objective of this work was to use high-throughput measurements with morphophysiological traits to characterize wheat genotypes in relation to water stress. Field experiments were conducted from May to September 2018 and 2019, using a sprinkler bar irrigation system to control water availability to eighteen wheat genotypes: BRS 254; BRS 264; CPAC 01019; CPAC 01047; CPAC 07258; CPAC 08318; CPAC 9110; BRS 394 (irrigated biotypes), and Aliança; BR 18_Terena; BRS 404; MGS Brilhante; PF 020037; PF 020062; PF 120337; PF 100368; PF 080492; and TBIO Sintonia (rainfed biotypes). The water regimes varied from 22 to 100% of the crop evapotranspiration replacement. Water stress negatively affected gas exchange, vegetation indices, and grain yield. High throughput variables TCARI, NDVI, OSAVI, SAVI, PRI, NDRE, and GNDVI had higher yield and morphophysiological measurement correlations. The drought resistance index indicated that genotypes Aliança, BRS 254, BRS 404, CPAC 01019, PF 020062, and PF 080492 were more drought tolerant.

Durable and impact-resistant thermoset polymers for the extreme environment of low Earth orbit

Polymers degrade rapidly in low-Earth orbit (LEO) due to extreme thermal cycling, solar radiation, micrometeoroid and orbital micro-debris impact, and the synergistic erosive effects of atomic oxygen (AO) with UV radiation. In this work, we investigate the synergistic effect of (AO+UV)-induced erosion and high-velocity impact damage on a tough thermoset, polydicyclopentadiene (pDCPD), via erosion yield measurements after a long-term LEO exposure in the ram orientation aboard the International Space Station (ISS) followed by ground-based hypervelocity impact tests. The incorporation of SiO2 nanoparticles reduces the AO-erosion rate by an order of magnitude, while also mitigating the associated degradation of surface mechanical properties. Moreover, the resistance of pDCPD to AO-erosion increases with crosslink density. Hypervelocity impact tests using sub-millimeter laser-driven flyer plates launched at 4.5 km s−1 reveal that pDCPD has higher impact resistance than epoxies with comparable erosion resistance. Interestingly, both epoxy and pDCPD exhibit a statistically insignificant change in impact resistance following surface AO-erosion during direct exposure to space vehicle ram conditions (7.8 km s−1) in LEO.