Yield Deviations Research Articles

Satellite Based Composite Weather Insurance Product for assessing mid-term adversary

The present study formulated a satellite-based rainfall insurance product (SRIP) at sub-seasonal scale during 2015–2020 by integrating meteorological, geophysical and vegetation index products e.g. rainfall (CHIRPS:Climate Hazards Group InfraRed Precipitation with Station), soil moisture (SMAP), normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) from moderate resolution imaging spectro-radiometer (MODIS). The SRIP was meticulously designed to integrate both cause (rainfall) and impact (NDVI, NDWI, soil moisture) parameters to enhance the assessment of min-season crop risks, with a particular emphasis on deficit rainfall. The performance evaluation of SRIP was conducted in two distinct agro-climatic zones viz. Deoria district of Uttar Pradesh (Upper Gangetic Plain Region) and Krishna district of Andhra Pradesh (Southern Plateau and Hills Region) spanned from June to October, which aligns with the major crop-growing season corresponding to the Southwest monsoon period in India. Three sets of SRIPs were investigated i.e. SRIP with cause parameter (SRIP1), SRIP with cause-impact parameters with no lag response (SRIP2) and SRIP with cause-impact parameters with one-month lag response (SRIP3). The SRIP was structured to provide a numerical index on a scale ranging from 0 to 1, representing poor to good crop-growing conditions that could effectively identify the rainfall deficit threshold (SRIP≤0.43) corresponding to negative percent deviation in rainfall (PDR≤−20%). This threshold level was recognized for compensation of triggers at the multiple crop growth phases during the crop-growing season. The district average of SRIP and PDR showed fair correlation co-efficient (0.77 ≤ r ≤ 0.88). Good correlation between sub-seasonal (monthly, bi-monthly, tri-monthly) SRIP and normalized MODIS Gross Primary Productivity (0.65 ≤ r ≤ 0.98), and SRIP deviation and yield deviation (0.45 ≤ r ≤ 0.97) reflect sensitivity of SRIP to crop carbon productivity and crop yield. The findings of the present study recommend SRIP not only a multi-phase composite index for assessing mid-season crop risks in terms of rainfall deficit for clusters of insurance units, but it has the potential to be used by insurance agencies to settle the claims by farmers in a transparent and faster manner for crop loss due to large-scale rainfall deficits. However, SRIP needs to be customized in future to make it more adaptable to settle insurance claims due to weather adversaries at individual farm level.

Application of the PSI cycle check-up methodology for full-core Monte Carlo simulations verified using pin power estimates and validated for hot zero power conditions

Monte Carlo (MC) based neutron transport codes provide high-fidelity numerical solutions to problems beyond the modelling capabilities of conventional core simulation methods. However, performing core-follow calculations with MC codes remains challenging due to the necessity of incorporating thermal-hydraulic feedback and generating evolved fuel composition for the cycle of interest by taking into account the entire irradiation history of loaded fuel assemblies over previous cycles. Nevertheless, at PSI, the neutronic version of a cycle check-up methodology (CHUP) is under development to address this challenge. This methodology involves extracting operating conditions and nuclide compositions from validated reference deterministic core-follow models (CASMO5/SIMULATE5/SNF), subsequently generating MC neutron transport models for selected operating points. This article presents the verification and validation performed for a hot zero power operating condition of a Swiss pressurised water reactor using the updated CHUP methodology, which automates information extraction from reference core models to minimise human intervention. The MC models were verified by assessing their deviation from criticality, consistently found to be supercritical within the [10,60] pcm range. Additionally, radial relative power distribution verification yield deviations in the [−5,4] % range compared to validated nodal results. Finally, additional validation was performed using start-up measurements from two successive cycles. Eight MC models were found to be subcritical, on average by 51 pcm, with a standard deviation of 25 pcm.

Enhanced synergies for product distributions and interactions during co-pyrolysis between corn stover and tyres

Co-pyrolysis has been studied for its potential to recycle energy from biomass and waste tyres, as well as enhance the quality of the bio-oil. In this paper, the TG-FTIR-GC/MS technique and a fast-infrared heated reactor were used to investigate the co-pyrolysis behaviors and mechanism of waste tyres (WT) and corn stover (CS). Based on the TG-FTIR-GC/MS analysis, co-pyrolysis synergy promoted the production of methane and aromatics while inhibiting the formation of CO2. Co-pyrolysis products distribution shows that the best synergy occurred at a ratio of 30 % WT with the highest oil yield deviation of 19.67 % and the lowest water yield deviation of −13.30 %. Response surface method (RSM) was utilized to optimize the oil production and the highest oil yield of 30.25 wt% was acquired at a heating rate of 25 °C/s and a ratio of 40 % WT. According to the oil analysis, the light fraction of oil (gasoline and diesel) was more than 50 % in all conditions and there was a large number of aromatics (more than 30 %) presented in oil. The char characteristics indicated that several metals combined with -S radicals during co-pyrolysis which formed much metal sulfide and sulfate in chars.

Evaluating the performance of herd-specific Long Short-Term Memory models to identify automated health alerts associated with a ketosis diagnosis in early lactation cows

The growing use of automated systems in the dairy industry generates a vast amount of cow-level data daily, creating opportunities for using these data to support real-time decision-making. Currently, various commercial systems offer built-in alert algorithms to identify cows requiring attention. To our knowledge, no work has been done to compare the use of models accounting for herd-level variability on their predictive ability against automated systems. Long Short-Term Memory (LSTM) models are machine learning models capable of learning temporal patterns and making predictions based on time series data. The objective of our study was to evaluate the ability of LSTM models to identify a health alert associated with a ketosis diagnosis (HAK) using deviations of daily milk yield, milk FPR, number of successful milkings, rumination time, and activity index from the herd median by parity and DIM, considering various time series lengths and numbers of d before HAK. Additionally, we aimed to use Explainable Artificial Intelligence method to understand the relationships between input variables and model outputs. Data on daily milk yield, milk fat-to-protein ratio (FPR), number of successful milkings, rumination time, activity, and health events during 0 to 21 d in milk (DIM) were retrospectively obtained from a commercial Holstein dairy farm in northern Indiana from February 2020 to January 2023. A total of 1,743 cows were included in the analysis (non-HAK = 1,550; HAK = 193). Variables were transformed based on deviations from the herd median by parity and DIM. Six LSTM models were developed to identify HAK 1, 2, and 3 d before farm diagnosis using historic cow-level data with varying time series lengths. Model performance was assessed using repeated stratified 10-fold cross-validation for 20 repeats. The Shapley additive explanations framework (SHAP) was used for model explanation. Model accuracy was 83, 74, and 70%, balanced error rate was 17 to 18, 26 to 28, and 34%, sensitivity was 81 to 83, 71 to 74, and 62%, specificity was 83, 74, and 71%, positive predictive value was 38, 25 to 27, and 21%, negative predictive value was 97 to 98, 95 to 96, and 94%, and area under the curve was 0.89 to 0.90, 0.80 to 0.81, and 0.72 for models identifying HAK 1, 2, and 3 d before diagnosis, respectively. Performance declined as the time interval between identification and farm diagnosis increased, and extending the time series length did not improve model performance. Model explanation revealed that cows with lower milk yield, number of successful milkings, rumination time, and activity, and higher milk FPR compared with herdmates of the same parity and DIM were more likely to be classified as HAK. Our results demonstrate the potential of LSTM models in identifying HAK using deviations of daily milk production variables, rumination time, and activity index from the herd median by parity and DIM. Future studies are needed to evaluate the performance of health alerts using LSTM models controlling for herd-specific metrics against commercial built-in algorithms in multiple farms and for other disorders.

Inductive cum targeted yield model-based integrated fertilizer prescription for sweet corn (Zea mays L. Saccharata) on Alfisols of Southern India.

Striking the right nutrient balance is essential for sustainable farming and ecosystem health. In this regard, field experiments were conducted in three phases viz., fertility gradient experiment, main experiment and validation experiment through a soil test crop response approach to develop and validate fertilizer prescription equations for sweet corn in comparison with general recommended dose and soil fertility rating approach. The soil data, fresh cob yield, and NPK uptake were used for establishing four important basic parameters, viz., nutrient requirement (kg t-1), contribution of nutrients from fertilizers, soil, and organic manure. The results revealed that nutrients required to produce one tonne of fresh cob yield (NR) were 5.85 kg, 0.87 kg and 4.31kg for N, P and K, respectively under the STCR NPK alone approach and 6.07 kg, 0.92 kg and 4.33 kg for N, P and K, respectively under STCR NPK+FYM approach. In the validation experiment, STCR NPK+FYM approach for the targeted yield of 25 t ha-1 recorded higher fresh cob yield (23.38 t ha-1) and dry stover yield (35.07 t ha-1) which were significantly higher compared to general recommended dose and soil fertility rating approach. The developed STCR equations for the aforesaid crop are valid as the percent deviation of cob yield from the targeted yield was within ±10%. Similarly, highest nutrient use efficiency was achieved with the STCR approach, specifically when targeting a lower yield through an NPK+FYM mode. Thus, implementation of the STCR approach of fertilizer prescription, with or without FYM, at targeted yields of 25 and 22 t ha-1, not only surpassed the effects of the other fertilizer recommendation approach in terms of cob yields, but also increased NPK uptake, improved nutrient use efficiency and greater economic returns.

Genome-wide association study for milking speed in Fleckvieh cattle

Milking speed is an important trait influencing udder health of dairy cows as well as labor efficiency. Yet, it has received little attention in genomic association studies. The main objective of this study was to determine regions and genes on the genome with a potential effect on milking speed in Fleckvieh (dual purpose Simmental) cattle. Genome-wide association studies were conducted using de-regressed breeding values of bulls as phenotypes. Six SNP on 4 autosomes were significantly associated with milking speed for additive effects. Significant regions on BTA4 and BTA19 correspond with findings for other dairy cattle breeds. Based on the observation of Fleckvieh breed managers, variation of milking speed in batches of daughters of some bulls is much higher than in daughter groups of other bulls. This difference in within family variation may be caused by transmission of alternative alleles of bulls being heterozygous for a gene affecting milking speed. To check on this, we considered standard deviation of yield deviations in milking speed of half-sib daughters as a new trait and performed GWAS for dominance effects. One signal on BTA5 passed the genome wide Bonferroni threshold that corresponded to the significant signal from standard GWAS on de-regressed breeding values. The key conclusion of this study is that several strong genomic signals were found for milking speed in Fleckvieh cattle and that the strongest of them are supported by similar findings in Brown Swiss and Holstein Friesian cattle. Milking speed is a complex trait whose sub-processes have not yet been elucidated in detail. Hence, it remains a challenge to link the associated regions on the genome with causal genes and their functions.

Creating a Lactation Model for 305-Day Milk Yield with Different Resampling Techniques (Bagging Mars) in Mars Modeling

The main purpose of this research is to obtain a prediction model for milk yield by using Multivariate Adaptive Regression Splines (MARS) and Bagging MARS algorithms as a non-parametric regression technique. For this purpose, the effects on milk yield of 305 days were investigated by using lactation parameters in dairy cattle. In the study, 9337 lactation milk yield records belonging to 37 animals belonging to the 2022-2023 period were used and the data set was created by randomly ordering the animals. Data on milk yield results were analyzed with MARS and Bagging MARS algorithms. For dairy cattle; it was modeled with explanatory variables such as lactation month (month), service period (SP), last 7 days average milk yield (L7DMMY), animal's first birth age (FP), animal's age (Age), number of lactations (LN).Correlation coefficient (r), coefficient of determination (R2), Adjusted R2, Root of Square Mean Error (RMSE), standard deviation ratio (SD ratio), mean absolute percent error (MAPE), mean absolute for MARS algorithm estimating total average milk yield deviation (MAD) and Akaike Information Criteria (AIC) values are 0.9986, 0.997, 0.977, 0.142, 0.052, 0.2389, 0.086 and -88, respectively. Similar statistics for the Bagging MARS algorithm are 0.754, 0.556, 0.453, 1.8, 0.666, 3.96, 1.47, and 115, respectively. It has been observed that MARS and Bagging MARS algorithms provide correct results according to the goodness of fit statistics. In this study, it was revealed that MARS algorithm gave better results in milk yield modeling of 305-day lactation.

Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1–3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1–30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (</= −15% YD), AMBER Group (−14% ̶ 0% YD), GREEN Group (>0% YD). In Step 3, yield, rumination, and physical activity data from days 1–3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1–3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

Current status and trends in sustainability of pea production in the Kemerovsk region

The demand for crop products is relatively constant in the short term, and in the long term, it increases relatively evenly. Consequently, the supply of products must be steadily steady or steadily increasing. Changes in hydrothermal conditions for cultivating crops lead to fluctuations in yield. The study was carried out based on statistical data from 2012 – 2021. The article examines the main trends in the development of pea (Pisum sativum L.) production in the steppe and forest-steppe zones of the Kemerovo region. The sown areas of peas and the crop yield are determined, and the characteristics of the gross grain harvest in the study zones are given. The trend line analysis showed an annual increase in the sown area; its value increased significantly from 2017 to 2021 in both the steppe and forest steppe. During the same period, a tendency was established towards a significant increase in pea yields due to improved farming standards. According to our data, the trend in the yield series is not the same. The coefficients of variation in the steppe zone are 38.51% and, in the forest-steppe zone - 34.73%, indicating a higher agrotechnical level in the forest-steppe zone than in the steppe zone. High coefficients in the studied areas are observed due to solid deviations in yield in individual years. In the steppe zone, the yield ranged from 6.9 to 23.9; in the forest-steppe, it went from 7.3 c/ha to 21.7. It was revealed that the size of the pea harvest strongly depends on weather conditions. The most productive years are 2020 and 2021. The trend equations for the steppe and forest-steppe describe the tendency towards an increase in the gross grain harvest. The conclusion is formulated that to solve the main problems in pea production, which depend on the farming culture and fluctuations in cultivation conditions, it is necessary to look for ways to increase the sustainability of the production of this crop.

Potential for quantifying general environmental resilience of dairy cattle in sub-Saharan Africa using deviations in milk yield.

Introduction: Genetic improvement of general resilience of dairy cattle is deemed as a part of the solution to low dairy productivity and poor cattle adaptability in sub-Saharan Africa (SSA). While indicators of general resilience have been proposed and evaluated in other regions, their applicability in SSA remains unexplored. This study sought to test the viability of utilizing log-transformed variance (LnVar), autocorrelation (rauto), and skewness (Skew) of deviations in milk yield as indicators of general resilience of dairy cows performing in the tropical environment of Kenya. Methods: Test-day milk yield records of 2,670 first-parity cows performing in three distinct agroecological zones of Kenya were used. To predict expected milk yield, quantile regression was used to model lactation curve for each cow. Subsequently, resilience indicators were defined based on actual and standardized deviations of observed milk yield from the expected milk yield. The genetic parameters of these indicators were estimated, and their associations with longevity and average test-day milk yield were examined. Results: All indicators were heritable except skewness of actual and standardized deviation. The log-transformed variance of actual (LnVar1) and standardized (LnVar2) deviations had the highest heritabilities of 0.19 ± 0.04 and 0.17 ± 0.04, respectively. Auto-correlation of actual (rauto1) and standardized (rauto2) deviations had heritabilities of 0.05 ± 0.03 and 0.07 ± 0.03, respectively. Weak to moderate genetic correlations were observed among resilience indicators. Both rauto and Skew indicators had negligible genetic correlations with both longevity and average test-day milk yield. LnVar1 and LnVar2 were genetically associated with better longevity (rg = -0.47 ± 0.26 and -0.49 ± 0.26, respectively). Whereas LnVar1 suggested that resilient animals produce lower average test-day milk yield, LnVar2 revealed a genetic association between resilience and higher average test-day milk yield. Discussion: Log transformed variance of deviations in milk yield holds a significant potential as a robust resilience indicator for dairy animals performing in SSA. Moreover, standardized as opposed to actual deviations should be employed in defining resilience indicators because the resultant indicator does not inaccurately infer that low-producing animals are inherently resilient. This study offers an opportunity for enhancing the productivity of dairy cattle performing in SSA through selective breeding for resilience to environmental stressors.

Risk prediction model of clinical mastitis in lactating dairy cows based on machine learning algorithms

Mastitis is the most common disease among dairy cows and is known to have negative effects on both animal welfare and the profitability of dairy farms. Early detection of clinical mastitis cases is considered the best option for preventing cows from developing mastitis. In this study, we developed clinical mastitis prediction models that only required inputting common indicators from the automatic milking system. We utilized multidimensional data from the cow mastitis database of Afimilk (China) Agricultural Technology Co., Ltd. to predict mastitis in dairy cows. All data were screened for the period of 0–150 days of lactation. The data included parity, lactation day, period, mean and standard deviation of milk yield, of electrical conductivity, and of lying time, which were taken as input features. The classification of whether cows suffer from clinical mastitis was determined as output. We analyzed 426 cows with clinical mastitis and 2087 healthy cows by using four machine learning algorithms: Decision Tree, Random Forest, Back Propagation neural networks, and Support Vector Machines. In these four algorithms, the accuracy ranged from 94% to 98%, while the running times varied widely from seconds to minutes. The decision tree prediction model achieved an accuracy of 98% and the precision rate for healthy cows was 99%, while for mastitis cows it was 97%. Machine learning algorithms have played an important role in predicting cow mastitis, with the Decision Tree algorithm showing great performance and higher accuracy in our research.

Genetic regions determine tolerance to nitrogen deficiency in European elite bread wheats grown under contrasting nitrogen stress scenarios.

Clustering 24 environments in four contrasting nitrogen stress scenarios enabled the detection of genetic regions determining tolerance to nitrogen deficiency in European elite bread wheats. Increasing the nitrogen use efficiency of wheat varieties is an important goal for breeding. However, most genetic studies of wheat grown at different nitrogen levels in the field report significant interactions with the genotype. The chromosomal regions possibly involved in these interactions are largely unknown. The objective of this study was to quantify the response of elite bread wheat cultivars to different nitrogen field stress scenarios and identify genomic regions involved in this response. For this purpose, 212 elite bread wheat varieties were grown in a multi-environment trial at different nitrogen levels. Genomic regions associated with grain yield, protein concentration and grain protein deviation responses to nitrogen deficiency were identified. Environments were clustered according to adjusted means for grain yield, yield components and grain protein concentration. Four nitrogen availability scenarios were identified: optimal condition, moderate early deficiency, severe late deficiency, and severe continuous deficiency. A large range of tolerance to nitrogen deficiency was observed among varieties, which were ranked differently in different nitrogen deficiency scenarios. The well-known negative correlation between grain yield and grain protein concentration also existed between their respective tolerance indices. Interestingly, the tolerance indices for grain yield and grain protein deviation were either null or weakly positive meaning that breeding for the two traits should be less difficult than expected. Twenty-two QTL regions were identified for the tolerance indices. By selecting associated markers, these regions may be selected separately or combined to improve the tolerance to N deficiency within a breeding programme.

Comparison of different validation methods for single-step genomic evaluations based on a simulated cattle population

The validation of estimated breeding values from single-step genomic BLUP (ssGBLUP) is an important topic, as more and more countries and animal populations are currently changing their genomic prediction to single-step. The objective of this work was to compare different methods to validate single-step genomic breeding values (GEBV). The investigations were carried out using a simulation study based on the German-Austrian-Czech Fleckvieh population. To test the validation methods under different conditions, several biased and unbiased scenarios were simulated. The application of the widely used Interbull GEBV test to the single-step method is only possible to a limited extent, partly because of genomic preselection, which biases conventional estimated breeding values. Alternative validation methods considered in the study are the Linear Regression method proposed by Legarra and Reverter, the improved genomic validation including extra regressions as suggested by VanRaden and an adaption of the Interbull GEBV test using (daughter) yield deviations ((D)YD) from ssGBLUP instead of pedigree BLUP. The comparison of the different methods for the different scenarios showed that for males the methods based on GEBV estimate the dispersion more accurate and less biased compared with the GEBV test using DYD from ssGBLUP, whereas the standard Interbull GEBV test is highly affected by genomic preselection for males. For females, the GEBV test using yield deviations from ssGBLUP results in better estimations for the true dispersion.

Investigating the effects of APSIM model configuration on model outputs across different environments

IntroductionSoil type plays a major role in nutrient dynamics and soil water which impacts crop growth and yield. The influence of soil characteristics on crop growth is usually evaluated through field experimentation (in the short term) and through crop-soil modelling (in the long-term). However, there has been limited research which has looked at the effect of model structural uncertainty of model outputs in different soil types.MethodsTo analyze the impact of soil inputs on model structural uncertainty, we developed eight model structures (a combination of two crop models, two soil water models and two irrigation models) within the Agricultural Production Systems sIMulator (APSIM) across three soil types (Ferralsols, Alisols and Chernozems). By decomposing the mean proportion of variance and simulated values of the model outputs (yield, irrigation, drainage, nitrogen leaching and partial gross margin) we identified the influence of soil type on the magnitude of model structural uncertainty.ResultsFor all soil types, crop model was the most significant source of structural uncertainty, contributing &amp;gt;60% to variability for most modelled variables, except irrigation demand which was dominated by the choice of irrigation model applied. Relative to first order interactions, there were minimal (&amp;lt;12%) contributions to uncertainty from the second order interactions (i.e., inter-model components). We found that a higher mean proportion of variance does not necessarily imply a high magnitude of uncertainty in actual values. Despite the significant impact of the choice of crop model on yield and PGM variance (contributing over 90%), the small standard deviations in simulated yield (ranging from 0.2 to 1 t ha-1) and PGM (ranging from 50.6 to 374.4 USD ha-1) compared to the mean values (yield: 14.6 t ha-1, PGM: 4901 USD ha-1) indicate relatively low actual uncertainty in the values. Similarly, the choice of irrigation model had a contribution of over 45% to variance, but the relatively small standard deviations ranging from 11 to 33.3 mm compared to the overall mean irrigation of 500 mm suggest low actual uncertainty in the values. In contrast, for the environmental variables- drainage and nitrogen leaching, the choice of crop model had contributions of more than 60% and 70% respectively, yet the relatively large standard deviations ranging from 7.1 to 30.6 mm and 0.6 to 7.7 kg ha-1 respectively, compared to the overall mean values of drainage (44.4 mm) and nitrogen leaching (3.2 kg ha-1), indicate significant actual uncertainty.DiscussionWe identified the need to include not only fractional variance of model uncertainty, but also magnitude of the contribution in measured units (e.g. t ha-1, mm, kg ha-1, USD ha-1) for crop model uncertainty assessments to provide more useful agronomic or policy decision-making information. The findings of this study highlight the sensitivity of agricultural models to the impacts of moisture availability, suggesting that it is important to give more attention to structural uncertainty when modelling dry/wet conditions depending on the output analyzed.

An Outlook on Processing Sweet Corn Production from the Last Three Decades (1990s–2010s)

Sweet corn (Zea mays L. var. rugosa or saccharate), consumed both fresh and processed (primarily canned or frozen), is a popular vegetable crop in the United States. Recent nationwide data have reported declining trends in sweet corn production, which poses serious challenges for the US processing sweet corn producers. Here, we evaluated a processing sweet corn dataset that represents nearly 3 decades (1992–2018) of commercial production in the Upper Midwest and the Pacific Northwest regions in an attempt to understand national trends in US processing sweet corn. The objectives of this study were to (a) quantify trends in processing sweet corn production (harvest area and total green ear mass production); (b) understand trends in planting date, plant population density, and hybrid lifespan; and (c) estimate interannual yield deviations in green ear mass yield. Our results reveal declining trends in sweet corn hectares, particularly in rainfed production systems of the Upper Midwest. For the past 3 decades of commercial sweet corn production, plant population density and planting dates used by vegetable processors have remained unchanged. Our analysis revealed a large range (1 to 20 years) in hybrid lifespan, which can be attributed to wide differences in hybrid yield stability across the diverse production environments in the United States. Rainfed production systems are becoming scarcer because sweet corn yields under rainfed conditions are particularly susceptible to severe weather, including heat and drought stress events. Future research is needed to understand sweet corn yields as a function of climatic and nonclimatic variables to stabilize the industry, particularly given predictions of a future with more frequent weather extremes.

Do the True Boiling-Point Distillation Yields of Crude Oil Blends Obey the Additive Blending Rule?

Twelve crude oil blends prepared from seven individual crude oils and an imported atmospheric residue were characterized through a true boiling point (TBP) distillation analysis and their density. When comparing the measured TBP fraction yields with those estimated through the application of the additive blending rule, it was found that, for four crude oil blends, the additive blending rule was valid, while for the remaining eight crude oil blends, deviations of the measured TBP yields from the estimated ones were bigger than the TBP analysis’s repeatability limits. By the use of intercriteria analysis evaluation of the data for the deviation of the TBP yields from the additive blending rule and the molar excess volume of the crude oil blends, statistically meaningful relations between the delta TBP yields of light and heavy naphtha, as well as vacuum residue with the molar excess volume, were found. The higher the magnitude of the crude oil blend’s molar excess volume, the bigger the deviations of the TBP yields of naphtha and vacuum residue are. The bigger the deviation of the crude oil blend’s behavior from that of the regular solution, as quantified by the molar excess volume, the bigger the deviations of the TBP yields of naphtha and vacuum residue are.

Application of single-step single nucleotide polymorphism best linear unbiased predictor model with unknown-parent groups for type traits in Japanese Holsteins

The objectives of this study were to investigate the computational performance and the predictive ability and bias of a single-step SNP BLUP model (ssSNPBLUP) in genotyped young animals with unknown-parent groups (UPG) for type traits, using national genetic evaluation data from the Japanese Holstein population. The phenotype, genotype, and pedigree data were the same as those used in a national genetic evaluation of linear type traits classified between April 1984 and December 2020. In the current study, 2 data sets were prepared: the full data set containing all entries up to December 2020 and a truncated data set ending with December 2016. Genotyped animals were classified into 3 types: sires with classified daughters (S), cows with records (C), and young animals (Y). The computing performance and prediction accuracy of ssSNPBLUP were compared for the following 3 groups of genotyped animals: sires with classified daughters and young animals (SY); cows with records and young animals (CY); and sires with classified daughters, cows with records, and young animals (SCY). In addition, we tested 3 parameters of residual polygenic variance in ssSNPBLUP (0.1, 0.2, or 0.3). Daughter yield deviations (DYD) for the validation bulls and phenotypes adjusted for all fixed effects and random effects other than animal and residual (Yadj) for the validation cows were obtained using the full data set from the pedigree-based BLUP model. The regression coefficients of DYD for bulls (or Yadj for cows) on the genomic estimated breeding value (GEBV) using the truncated data set were used to measure the inflation of the predictions of young animals. The coefficient of determination of DYD on GEBV was used to measure the predictive ability of the predictions for the validation bulls. The reliability of the predictions for the validation cows was calculated as the square of the correlation between Yadj and GEBV divided by heritability. The predictive ability was highest in the SCY group and lowest in the CY group. However, minimal difference was found in predictive abilities with or without UPG models using different parameters of residual polygenic variance. The regression coefficients approached 1.0 as the parameter of residual polygenic variance increased, but regression coefficients were mostly similar regardless of the use of UPG across the groups of genotyped animals. The ssSNPBLUP model, including UPG, was demonstrated as feasible for implementation in the national evaluation of type traits in Japanese Holsteins.

Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats

The shape of the lactation curve is linked to an animal's health, feed requirements, and milk production throughout the year. Random regression models (RRM) are widely used for genetic evaluation of total milk production throughout the lactation and for milk yield persistency. Genomic information used with the single-step genomic BLUP method (ssGBLUP) substantially improves the accuracy of genomic prediction of breeding values in the main dairy cattle breeds. The aim of this study was to implement an RRM using ssGBLUP for milk yield in Saanen dairy goats in France. The data set consisted of 7,904,246 test-day records from 1,308,307 lactations of Saanen goats collected in France between 2000 and 2017. The performance of this type of evaluation was assessed by applying a validation step with data targeting candidate bucks. The model was compared with a nongenomic evaluation and a traditional evaluation that use cumulated performance throughout the lactation model (LM). The incorporation of genomic information increased correlations between daughter yield deviations (DYD) and estimated breeding values (EBV) obtained with a partial data set for candidate bucks. The LM and the RRM had similar correlation between DYD and EBV. However, the RRM reduced overestimation of EBV and improved the slope of the regression of DYD on EBV obtained at birth. This study shows that a genomic evaluation from a ssGBLUP RRM is possible in dairy goats in France and that RRM performance is comparable to a LM but with the additional benefit of a genomic evaluation of persistency. Variance of adjacent SNPs was studied with LM and RRM following the ssGBLUP. Both approaches converged on approximately the same regions explaining more than 1% of total variance. Regions associated with persistency were also found.

Construction of coaxial liquid centrifugal platform for the extraction of active ingredients from natural products.

In the classical natural product extraction and separation process, it is tedious and requires large amounts of reagents and time. In this study, an efficient coaxial liquid centrifugal oil-water-oil triple-liquid-phase system with a simple structure and convenient operation was successfully constructed and used to extract flavonoids from Platycladi Cacumen. The results showed that the coaxial liquid centrifugal platform constructed in this study had good stability and 6ml was the minimum volume of the middle phase for 1000rpm to stabilize the system. Besides, it was easy to repeat the operation: the relative standard deviations of the extraction yields of flavonoids and sugar in six parallel operations were all less than 10%. Moreover, it was only one-tenth of the time required for this method as traditional liquid-liquid extraction while reducing the use of volatile organic reagents. Finally, the new method was more selective than the traditional method for the extraction of flavonoids. Therefore, this study provides a possibility for the coaxial liquid centrifugal platform to be used in multi-liquid phase systems to achieve the simultaneous extraction of different parts of natural products by different liquid phases. It is expected to provide a reliable reference for further expansion of small-scale experimental operations to industrial production.

Метод прогноза урожайности семян подсолнечника по субъектам европейской части и России в целом с заблаговременностью 3–3,5 месяца

A method is developed for forecasting the yield of sunflower seeds for the subjects of the European part of Russia and entire Russia based on ground observations of Roshydromet hydrometeorological stations. The sunflower yield was used according to Rosstat data in weight after processing. When developing the forecast method for the subjects of the Russian Federation, the predicted sunflower yield was considered as the sum of two forecasts: the forecast of the yield trend described by degree 1 and 2 polynomials and the forecast of yield anomalies (yield deviations from the trend) depending on meteorological and agrometeorological factors and most affecting the yield. The criterion for selecting parameters for the predictive regression prediction model for different subjects was a degree of influence of this parameter on the yield. The predictive regression model for calculating yields in entire Russia was developed on the basis of the principal component analysis using yields for federal districts expressed in yield deviations from the average long-term yield for specific districts. The method for forecasting the sunflower yield was adopted as the main one at Hydrometeorological Research Center of Russia and for the subjects on the territory of the Bashkir, Volga, Crimean, Central Chernozem, Tatarstan, and North Caucasian administrations for hydrometeorology and environmental monitoring (the Volgograd, Rostov, Krasnodar, Stavropol regions, the Kabardino-Balkarian Republic). Ключевые слова: sunflower, yield, forecast method, operational tests, accuracy