Robust Multiple Regression Models Research Articles

Impact of climate change and genetic development on Iowa corn yield

The vulnerability of corn yield to high temperature and insufficient rainfall in the US mid-west is widely acknowledged. The impact of extreme weather and genetic development on corn yield is less well known. One of the main reasons is that the multicollinearity in the variables can lead to confounding results. Here we model the impact of climate and genetic development by employing an elastic net regression model to address the multicollinearity issue. This allows us to develop a more robust multiple regression model with higher predictive accuracy. Using granular data for Iowa from 1981-2018, we find that corn yield is vulnerable to high mean summer temperatures particularly in July, a widening diurnal temperature range in June and dry summer conditions (due to extremely low rainfall) from June-August. We find that overall climate impact reduced average annual yield by 0.7%. We also find that genetic development which led to earlier planting dates, widening duration of the reproductive interval, higher growing degree day accumulation and larger net planted area had a beneficial impact on the Iowa corn yield during 1981-2018 resulting in an average annual yield improvement of 1.8% per annum. This provides a basis for optimism that these genetic developments and management practices will continue to adapt and improve in the future to counter the impact of climate change on corn yield. We have also modelled the impact of future climate change using the latest climate projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). These climate projections show that the average temperature during the growing season (MayO-October) will increase by 2.4 -2.9 o C by mid-century while the average spring temperature (March and April) will increase by a relatively slower 1.9 -2.3 o C by mid-century. Additionally, climate projections show that both temperature and rainfall will also become more extreme in the future with the changes varying from spring to summer. Our results show that, just due to climate change alone in Iowa corn yield will decline between 1.4-1.7% per annum until mid-century (or 1.2-2.1% per annum until the late twenty first century).

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation.

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.

The effect of the level of mixer-added water and mash conditioning temperature on parameters monitored during pelleting and phytase and xylanase thermostability

Exogenous enzymes have been used in broiler and swine rations to improve nutrient utilization and reduce feed costs, both of which improved the efficiencies of live production. Feed manufacturers have also utilized practices, such as warm mash conditioning temperatures (CT) and the addition of moisture at the mixer or mash conditioning chamber, to maintain adequate physical feed quality. While the impact of warm conditioning practices on enzyme thermostability had been well described, the impact of mixer-added water (MAW) on enzyme thermostability was scarcely investigated in previous literature. The present experiment intended to investigate the interaction of three mash CT and three levels of MAW on the thermostability of a phytase and xylanase mixer-added enzyme within a swine gestation diet in a 3 × 3 factorial randomized complete block design. The mash CT included 80, 86, and 92 °C and the levels of MAW included 0, 10, and 20 g/kg. Parameters monitored during the pelleting process included pellet durability index (PDI), pellet mill energy consumption (PMEC), and the change in temperature between hot pellets and conditioned mash (Δ T). Conditioning mash at 92 °C resulted in a 22.5 percentage-point reduction in phytase recovery in conditioned mash and a 70.1 percentage-point reduction in phytase recovery in pellets when compared to diets pelleted at 80 °C (P < 0.01). The xylanase, however, was not affected by mash CT. Phytase and xylanase recovery in pellets was not affected by the level of MAW. As PDI and PMEC increased, phytase recovery in pellets relative to unconditioned mash decreased (P < 0.01). As Δ T increased, phytase recovery in pellets relative to unconditioned mash increased (P < 0.01). Xylanase denaturation was poorly described by the parameters monitored during pelleting, likely because it was minimally denatured during pelleting. A robust multiple regression model was generated to predict phytase activity in pellets relative to unconditioned mash (R2 = 0.95; P < 0.01) when the controlled factors and monitored parameters were combined in a predictive model. In conclusion, the primary site of enzyme denaturation in a pilot scale pellet mill was the pellet mill die and MAW did not affect enzyme recovery.

Atmospheric Instability Dominates the Long‐Term Variation of Cloud Vertical Overlap Over the Southern Great Plains Site

AbstractAccurate representation of cloud vertical overlap in climate models is particularly significant for predicting the total cloud fraction (TCF) and calculating radiative budget. It refers to the parameterization of overlap parameter—decorrelation length scale L—but the potential of dynamical factors in developing parameterization of L has still received far less attention. Using ground‐based radar observation over Atmospheric Radiation Measurement Southern Great Plains site, here long‐term seasonal‐averaged L is retrieved and shows a very high anticorrelation with TCF from different data sets, indicating that TCF is sensitive to the way of cloud overlap. Therefore, combined with meteorological reanalysis data set, a robust multiple regression model between L and dynamical factors is built and exhibits smaller TCF bias compared with previous parameterization of L. Contribution calculation further verifies that atmospheric instability contributes 70% of L variation, indicating that it dominates the long‐term variation of L over Southern Great Plains site. This finding implies that dynamical factors should be taken into account in the parameterization of L.

Methanol Poisoning as an Acute Toxicological Basal Ganglia Lesion Model: Evidence from Brain Volumetry and Cognition.

Acute methanol poisoning leads to optic neuropathy and necrotic lesions of basal ganglia (BG) and subcortical white matter. Survivors of methanol poisoning exhibit long-term executive and memory deficits. Associations between brain volumetry parameters and cognitive sequelae of methanol poisoning are not known. The aim of our study was to identify long-term associations between the cognitive performance of survivors of methanol poisoning and the volume of the brain structures that are selectively vulnerable to methanol. We conducted a cross-sectional follow-up study on a sample of patients (n=33, age 50±14 years, 82% males) who survived acute methanol poisoning during methanol mass poisoning outbreak from September 2012 till January 2013 in the Czech Republic. A battery of neuropsychological tests and brain magnetic resonance imaging were included in the clinical examination protocol. Specific brain structures (putamen, globus pallidus, nucleus caudatus, and frontal white matter) were selected as regions of interest, and their volumes were estimated using the MorphoBox prototype software. In robust multiple regression models, sustained visual attention performance (as assessed by Trail Making Test and Prague Stroop Test) was positively associated with BG structures and frontal white matter volumes (Wald=9.03 to 85.50, p<0.01), sensitivity to interference (as assessed by Frontal Battery Assessment) was negatively associated with frontal white matter volume (Wald=35.44 to 42.25, p<0.001), and motor performance (as assessed by Finger Tapping Test) was positively associated with globus pallidus and frontal white matter volumes (Wald=9.66 to 13.29, p<0.01). Our results demonstrate that smaller volumes of elements of BG-thalamocortical circuitry, namely the BG and frontal white matter, relate to attention and motor performance in methanol poisoning from a long-term perspective. Disruption of those functional circuits may underlie specific cognitive deficits observed in methanol poisoning.

The influence of research and development policy: the case of Estonia in the EU

Purpose – The purpose of this paper is to evaluate the international position of Estonia among the member states of the EU and countries closely associated with the EU, from the perspective of the effect of research and development (R &amp; D) policy on innovation activities in the business sector. Design/methodology/approach – Based on existing scientific research literature on the relationships between R &amp; D policy and business sector R &amp; D activities and innovation performance, a set of indicators describing R &amp; D policy measures was created for the business sector. Using principal component analysis (PCA) method, independent robust dimensions of R &amp; D policy were brought out. After eliminating the problem of multicollinearity in R &amp; D policy indicators, robust multiple regression models were conducted to present a comprehensive empirical description of the shaping of business sector R &amp; D and innovation activities in the sample of investigated countries. Findings – Based on the literature, the influences of R &amp; D policy measures on business sector R &amp; D activities and innovation performance were systemised; public R &amp; D policy dimensions were empirically defined; the intensity of R &amp; D policy influence on business sector R &amp; D activities was estimated; the differences between real and prognostic values of business sector performance indicators in Estonia were calculated in order to characterise the efficiency of Estonian R &amp; D policy and the influence of the socioeconomic environment. Research limitations/implications – The lack of comparable data describing R &amp; D policy and R &amp; D activities and innovation performance in the business sector limits the comprehensiveness of the analysis (i.e. the number of analysed indicators). Practical implications – The assessment and comparative analysis of the influence of R &amp; D policy components on business sector R &amp; D activities and innovation performance in different countries makes it possible to identify directions for increasing the efficiency of R &amp; D policy under the specific influence of the socioeconomic environment, especially in new member states of the EU. Originality/value – Using the PCA method significantly increased the robustness of the macro-quantitative description of R &amp; D policy dimensions. By combining the set of new synthetic R &amp; D policy indicators created by the PCA with the multiple regression analysis method, a significant increase in the robustness of model coefficients (i.e. the assessments of influence intensity) was achieved. These robust models create the basis for reliable empirical assessment of the influence of R &amp; D policy and a comparative analysis of the results.