Real Yield Research Articles

Study of the varietal behavior of 07 durum wheat varieties (Triticum durum Desf.) in the Oued Righ Valley (ITDAS Station)

Cereal production in the Oued Righ Valley is limited by several factors, including high temperatures and the salinity of irrigation water. These constraints cause significant variations in crop behavior and yield. This study aims to evaluate the performance of seven durum wheat varieties at the El Arfiane demonstration and seed production farm (ITDAS) in the Oued Righ Valley in the south East of Algeria. The evaluation focuses on morphological parameters and yield components To identify the variety or varieties that demonstrate favorable performance and yield, thus encouraging local farmers to cultivate them. The results of this study indicate significant differences in morphological parameters such as plant height, spike length, number of spikes per square meter, and number of grains per spike. However, other measured parameters, such as the number of tillers per plant, thousand-grain weight TGW, grain yield, and harvest index, The methodology employed in this study follows a completely randomized block design with three replications. The experimental design includes a set of experimental units arranged in blocks, with treatments randomly assigned within each block, hence the term "completely randomized block." Each treatment appears only once in each block. The blocks are typically placed contiguously to facilitate observations and task execution. This design is widely used in agronomy as it simplifies task execution and the comparison of treatments. Despite an acceptable thousand-grain weight this year, ranging from 37.06g for variety T4 to 27.6g for variety T1, the yields were low. The highest biological yield was 33.11 quintals per hectare for variety T5, while the lowest yield was 11.39 (T2) quintals per hectare. Several factors, including pigeon attacks and harvest losses, contributed to the reduced real yield.

Do Real Estate Investments Yield the Highest Returns in Pakistan?

The investment choices of economic agents are critical in determining the value of capital stock in different sectors of the economy. These choices depend upon the available investment options in society and the expected rate of returns. In the case of Pakistan, it is commonly perceived that real estate investment in the form of buying a piece of land or buying a house comparatively yields the highest returns. This study aims to devolve on this presumption and estimate returns from different available investment avenues. Our findings, contrary to common perception, suggest investment in stocks yields the highest real returns while real estate returns stand second. However, the associated risk with stock investment is higher than in the real estate sector. Therefore, one can argue that real estate investment offers better returns comparatively. Surprisingly, we also find that the real returns from treasury bills, national savings accounts, and silver are negative. We also delve into the potential determinants of real estate returns and their impact on investment outcomes. The estimation results reveal that GDP growth, interest rate, and inflation pandemic are significantly affecting the rate of returns from real estate, while the Covid pandemic has negatively affected the returns from housing.

Crop yield estimation at different growing stages using a synergy of SAR and optical remote sensing data

Crop yield forecasting is an essential component of crop production assessment, impacting people at the global scale down to the level of individual farms. Until now, yield forecasting has predominantly relied on optical data, particularly the maximum value of vegetation indexes. However, this approach only presents a short forecasting window, and it is essential to obtain yield estimates as early as possible in the growing season and then further improve forecasting even after the vegetation index has reached its peak. So far, optical satellite data at high-temporal resolution (1–3 days) has been actively used for real time crop yield monitoring, whereas fewer operational models make a use of synthetic aperture radar (SAR). In this study, we explore whether SAR data can capture distinct aspects of crop dynamics, providing new insights for yield estimation depending on the crop's phenological stage. We assess the efficiency of dual- (Sentinel-1) and quad-polarimetric (UAVSAR, RADARSAT-2) data to explain inter-field crop yield variability for corn, soybean, and rice over a test area in Arkansas, US (258 fields, 2019). We used optical imagery acquired by Planet/Dove-Classic, Sentinel-2, and Landsat 8, to establish a baseline performance of satellite-based indicators to explain yield variability and assess dual- and quad-polarimetric SAR data for crop yield assessment. In terms of polarimetric indexes, the results showed that in general the results for rice were mostly stable and better than the other crops (R2adj ∼ 0.4 on average). The best results were obtained for the Sentinel-1 VHasc with R2adj = 0.47 and RADARSAT-2 phase difference with R2adj = 0.45. The results for corn performed the least with an R2adj <0.35 for all the indexes. The results for soybeans were more variable and were highly correlated with certain indicators such as RADARSAT-2 HV, RADARSAT-2 Volume, and RADARSAT-2 Pauli HV with R2adj>0.4. We also investigated the day of year (DOY) with the maximum correlation between optical and SAR-derived features and the final yields for corn, soybean, and rice. The maximum correlation for optical features occurs over a short time between DOY 155 (June 4) and 185 (July 5) for corn and rice, and DOY 190 (July 9) and DOY 211 (July 30) for soybean, with these results being consistent across various optical-based sensors. On the contrary, the maximum correlation for SAR-derived features varied significantly and was between DOY 120 (April 30) to DOY 225 (August 13). A study of the time series parameters cross-correlation showed that the optical parameters were highly correlated, but the SAR parameters showed strong temporal decorrelation. We conducted a comparison between C-band and L-band to assess their sensitivity at each stage of growth. In this experiment, we determined that for low vegetation, the C band will be more useful at the beginning of the growth cycle, while the L band provides more information in later stages of growth. Using a random forest regression model combining SAR parameters with common difference vegetation index (DVI), we improved the error by 50% in comparison to the error using the (DVI) for corn, soybean, and rice.

Brittle and ductile yielding in soft materials

Many soft materials yield under mechanical loading, but how this transition from solid-like behavior to liquid-like behavior occurs can vary significantly. Understanding the physics of yielding is of great interest for the behavior of biological, environmental, and industrial materials, including those used as inks in additive manufacturing and muds and soils. For some materials, the yielding transition is gradual, while others yield abruptly. We refer to these behaviors as being ductile and brittle. The key rheological signatures of brittle yielding include a stress overshoot in steady-shear-startup tests and a steep increase in the loss modulus during oscillatory amplitude sweeps. In this work, we show how this spectrum of yielding behaviors may be accounted for in a continuum model for yield stress materials by introducing a parameter we call the brittility factor. Physically, an increased brittility decreases the contribution of recoverable deformation to plastic deformation, which impacts the rate at which yielding occurs. The model predictions are successfully compared to results of different rheological protocols from a number of real yield stress fluids with different microstructures, indicating the general applicability of the phenomenon of brittility. Our study shows that the brittility of soft materials plays a critical role in determining the rate of the yielding transition and provides a simple tool for understanding its effects under various loading conditions.

Inflation Expectations, Liquidity Premia and Global Spillovers in Japanese Bond Markets

We provide market-based estimates of Japanese inflation expectations using an arbitrage-free dynamic term structure model of nominal and real yields that accounts for liquidity premia and the deflation protection afforded by Japanese inflation-indexed bonds, known as JGBi’s. We find that JGBi liquidity premia exhibit significant variation, and even switch sign. Properly accounting for them significantly lowers the estimated value of the indexed bonds’ deflation protection and affects inflation risk premium estimates. After liquidity adjustment, long-term Japanese inflation expectations have remained relatively stable at levels modestly exceeding one percent during the pandemic period. We then utilize our estimated liquidity measure to confirm the existence of statistically significant and economically meaningful spillovers to the JGBi market from global bond market illiquidity, as proxied by periods of low U.S. Treasury market depth.

Discounting the Distant Future: What Do Historical Bond Prices Imply about the Long-Term Discount Rate?

We present a thorough empirical study on real interest rates by also including risk aversion through the introduction of the market price of risk. From the viewpoint of complex systems science and its multidisciplinary approach, we use the theory of bond pricing to study the long-term discount rate to estimate the rate when taking historical US and UK data, and to further contribute to the discussion about the urgency of climate action in the context of environmental economics and stochastic methods. Century-long historical records of 3-month bonds, 10-year bonds, and inflation allow us to estimate real interest rates for the UK and the US. Real interest rates are negative about a third of the time and the real yield curves are inverted more than a third of the time, sometimes by substantial amounts. This rules out most of the standard bond-pricing models, which are designed for nominal rates that are assumed to be positive. We, therefore, use the Ornstein–Uhlenbeck model, which allows negative rates and gives a good match to inversions of the yield curve. We derive the discount function using the method of Fourier transforms and fit it to the historical data. The estimated long-term discount rate is 1.7% for the UK and 2.2% for the US. The value of 1.4% used by Stern is less than a standard deviation from our estimated long-run return rate for the UK, and less than two standard deviations of the estimated value for the US. All of this once more reinforces the need for immediate and substantial spending to combat climate change.

Nitrogen sensing and regulatory networks: it's about time and space.

A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its "hit-and-run" mode of target gene regulation, and temporal transcriptional cascade identified by "network walking." Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.

Copula Graphical Models for Heterogeneous Mixed Data

This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contain groups of samples obtained under heterogeneous conditions in space and time, potentially resulting in differences in network structure among groups. Therefore, the iid assumption is unrealistic, and fitting a single graphical model on all data results in a network that does not accurately represent the between group differences. In addition, real-world observational data is typically of mixed discrete-and-continuous type, violating the Gaussian assumption that is typical of graphical models, which leads to the model being unable to adequately recover the underlying graph structure. Both these problems are solved by fitting a different graph for each group, applying the fused group penalty to fuse similar graphs together and by treating the observed data as transformed latent Gaussian data, respectively. The proposed model outperforms related models on learning partial correlations in a simulation study. Finally, the proposed model is applied on real on-farm maize yield data, showcasing the added value of the proposed method in generating new production-ecological hypotheses. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=heteromixgm. Supplementary materials for this article are available online.

Debt-financed fiscal stimulus in South Africa

Levels of government debt-to-GDP have been rising substantially for over a decade in emerging market economies such as South Africa, which has led to much debate around the implementation of large-scale debt-financed fiscal stimulus programs in response to the economic fallout of the global covid-19 pandemic. Debt-financed fiscal policies directly stimulate aggregate demand through government expenditure or tax cuts, but their effectiveness is highly dependent on direct crowding-out of private sector expenditure, spill-over effects to the private sector through a higher risk premium on interest rates, and the interaction between fiscal policy and monetary policy. Using an open-economy dynamic stochastic general equilibrium model for South Africa, we identify the effect of six different fiscal policy instruments on short-term and long-term interest rates. These disaggregated expenditure and revenue shocks raise long-term real yields between 18 and 29 basis points, but there are non-negligible differences in the dynamic responses to each fiscal instrument. Our main findings suggest that, in the context of fiscal sustainability, an investment-driven debt-financed fiscal stimulus programme would reduce the government debt-to-GDP ratio, especially in periods of economic slack when monetary policy would typically be more accommodative. In fact, since the global financial crisis, monetary policy has reduced the burden of fiscal adjustment in response to rising debt and a rising risk premium. But further shocks to the risk premium could offset any gains from the current stance of monetary policy (for example, a credit rating shock raises the long-term government bond rate 155 basis points).

Salience effect and yield curve

This paper studies the influence of salience on nominal and real yield curves by introducing the salience effect in the Piazzesi and Schneider model (hereafter PS). We construct the salience values based on the expected consumption growth using U.S. data. We find that salience values are negatively correlated with the expected consumption growth rates. Based on U.S. data from 1960q1 to 2020q4, we find that the salience model can generate upward nominal and real yield curves within reasonable risk aversion coefficients (less than 10), as well as well-fitted average yields with actual data. The salience model compensates for the PS or recursive preference model’s inability to generate an upward nominal or real yield curve within reasonable risk aversion. Furthermore, we provide empirical support for model implications.

Fuzzy and rough approach to the problem of missing data in fall detection system

Two new methods for mining incomplete data based on interval-valued fuzzy set theory and rough set theory, particularly maximal consistent blocks were proposed, and their application to the fall detection system, exactly to the posture detection system. The suggested methods are based on interval-valued aggregation operators supporting decision-making involving uncertainty. Additionally, the new measure of knowledge was used in two aspects: in the imputation of missing data and during the inference process. The usage of the knowledge measure allowed for a reduction of the uncertainty due to incompleteness and imprecision of data, while increasing the efficiency of the decision-making process, through the additional selection of objects for which the degree of the information measure is the highest. The developed hybrid approach to mining incomplete data and its application in a real decision-making problem yield stable performance despite the increase in missing data.

&lt;span&gt;A New Monetary Policy Tool: The Real Neutral Rate Yield Curve for Canada&lt;/span&gt;

&lt;span&gt;A New Monetary Policy Tool: The Real Neutral Rate Yield Curve for Canada&lt;/span&gt;

Corporate Real Estate Values and Bond Yield Spreads

Corporate Real Estate Values and Bond Yield Spreads

Preservation of ex-situ gene pool of rare medicinal plant species in Kazakhstan

The approved “List of Medicinal Plants” consists of 278 medicinal plants used in official or traditional medicine. At the same time, 65 species have been listed in the Red Book of Kazakhstan. In the experimental medicinal plant collections located in the foothills of Zailiyskiy Alatau (in the Main Botanical Garden, Almaty), a total of 48 (74%) are indigenous medicinal herbs of Kazakhstan’s flora were grown over different years. A significant number of rare medicinal plant species of Kazakhstan (37 species), which is more than half of all tested ones, can be successfully grown. Eight of them are blooming and bearing fruit regularly and tree crops give real yields. Thirteen species of trees and shrubs have high adaptive abilities and were introduced at the expositions of the botanical garden. Sixteen species could bloom but not all of them produced seeds consistently. Introduction of 13 species are very difficult or unsuccessful, 17 species (26%) of the medicinal flora have not been tested in culture, mainly due to the known futility of cultivation in the open ground.

Optimizing forage harvest and the nutritive value of Italian ryegrass-based mixed forage cropping under northwestern Himalayan conditions.

The scarcity of high-quality forage has a significant influence on the productivity and profitability of livestock. Addressing this concern, an investigation was undertaken to assess the effects of distinct Italian ryegrass genotypes, namely, Punjab ryegrass-1, Kashmir collection, and Makhan grass, in conjunction with varying seeding ratios of Italian ryegrass to Egyptian clover. The seeding ratios considered were 100:0 (Italian ryegrass to Egyptian clover), 75:25, 50:50, and 25:75. All possible combinations of Italian ryegrass and Egyptian clover with seeding ratios were set up in a randomized complete block design and replicated thrice. Co-cultivating Italian ryegrass and Egyptian clover at a 75:25 seeding ratio yields the best yield benefit, as determined by the land equivalent ratio. It is noteworthy that in this configuration, real yield loss is higher for Egyptian clover and for Italian ryegrass when the seeding ratio is 25:75. The higher competitiveness of Italian ryegrass in comparison to Egyptian clover is highlighted by the competitive ratio. Notably, the nutritive parameter, crude protein yield, was significantly higher in the Makhan grass-based 50:50 and 75:25 seeding ratio. Results of the study ascertained the compatibility of grass-legume co-cultivation with significantly higher quantity and quality forage harvested under mixed cropping systems whereas Makhan grass as the superior and dominant genotype in comparison to Kashmir collection. The outcomes of this study revealed that the 100:0 seeding ratio, coupled with the Makhan grass genotype, exhibited superior performance in terms of cumulative forage harvest, dry matter accumulation, net returns, and benefit-cost ratio.

Extraction of agavins from Agave durangensis leaves: Structural, thermal and techno-functional characterization

This research aimed to optimize the extraction process of the agavins from Agave durangensis leaves and to carry out their structural, thermal, and techno-functional characterization. From the optimization methodology, a theoretical yield of 41.40 g agavins/g d.s. and a real yield of 40.31 g agavins/100 g d.s. were obtained. In addition, a characteristic carbohydrate profile by TLC and the functional groups by FT-IR were found. DSC analysis showed an endothermic peak between 65.43 and 210 °C, while the glass transition temperature (Tg) ranged between 34.92 and 52.66 °C. The particle size showed two principal distributions with good polydispersity. The techno-functional properties of moisture, wettability and bulk density were similar to those of commercial Agave tequilana agavins, while color, hygroscopicity, and water activity were statistically different. This work is a basis for using Agave durangensis leaves agavins in food applications and future scientific research.

A novel approach for incidence angle modifier calculation of arbitrarily oriented linear Fresnel collectors: Theory, simulations and case studies

The easiest possibility to assess the combined opto-energy efficiency of Linear Fresnel Collectors (LFC) is to refer to the Incidence Angle Modifier (IAM) concept: for this kind of solar collector, IAM calculation is often performed according to the so-called factorization method. Unfortunately, the factored IAM fails in providing reliable results of the real LFC yield. In this investigation, hourly 3D ray tracing simulations have been carried out on two case-study plants located in Morocco and Italy: reference ray tracing calculated IAMs have been used to develop simple correlations for inferring the instantaneous IAM of arbitrarily aligned LFCs. Instantaneous IAM formulas resulted to be able to provide yield predictions with a Root Mean Square Error equal to 5.57 % of the yearly average IAM, much lower than the factorization method (19.42 %). Subsequently, a reduced 3-day dataset of ray tracing values has been considered to calibrate the regression constants, resulting in still high accuracy for orientations not exceeding 30°. Finally, the analysis of available power at the receiver allowed to further assess the robustness of the proposed approach regardless of the plant orientation angles.

Taxation of Treasury Inflation Protected Securities

Inflation is back, causing problems for taxpayers looking for safe, inflation-protected investments. This Article engages in an in-depth analysis of an understudied part of the Internal Revenue Code and associated Treasury Regulations section 1.1275-7, the inflation-indexed debt rules, asking whether Treasury Inflation Protected Securities (TIPS) can act as a suitable hedge against inflation for taxable investors. TIPS are often considered tax-disadvantaged instruments, and this Article questions the extent to which that is true. I examine TIPS relative to inflation, nominal Treasuries and I Bonds, and in doing so make three important contributions to tax scholarship. First, I present original empirical analysis on financial data from the 2003-22 period, finding that TIPS are taxed at least as favorably as nominal Treasuries on a buy-and-hold basis. Under certain assumptions, like interest withholding, the section 1.1275-7 regime imposes less onerous tax consequences on TIPS than on nominals. Second, my results confirm that the performance of TIPS relative to nominal Treasuries depends on how unanticipated inflation deviates from expected inflation. Analysis of the 2003-21 period shows that nominals outperformed TIPS on both a pre-and a post-tax basis, while analysis of the 2003-22 period shows that 10-year TIPS issued between 2012 and 2022 generally outperformed nominals precisely because of the rise in unanticipated inflation in 2021 caused by the response to COVID-19. Third, I find that since 2011 all riskless bonds have generally had negative real yields on an after-tax basis. These findings have important implications for current tax policy since they show that taxpayers cannot access positive real riskless returns on an after-tax basis. With the world in a period of rising interest rates and high inflation, this suggests that it may be time to reconsider the canonical emphasis on nominal rather than real taxation.

Clinical value of insertable loop recorder: single centre experience

Abstract Background The aetiology of syncope can be challenging to undermine because of its sporadic nature. Implantable loop recorders (ILR) are recommended to be used early in undetermined syncope to establish a symptom – rhythm correlation if cardiac syncope is suspected. Real world diagnostic yield of ILR is not well established. Purpose To evaluate the diagnostic yield of ILR devices in patients with undetermined syncope. Methods This single centre, retrospective study, reviewed the electronic medical records of all patients, who underwent ILR insertion for syncope from January 2017 to June 2022. Abnormal rhythm strips on ILR were reviewed by a cardiac physiologist and cardiology consultant to determine its significance. Primary outcome was to determine the total number of cardiac implantable electronic devices (CIEDs) inserted due to ILR findings. Secondary outcomes included (1) a baseline characteristics comparison between the group of patients who had an arrhythmia detected on ILR and patients who had detected no arrhythmia, and (2) the total number of clinical significant events (CSEs), defined as a symptomatic arrhythmia, an asymptomatic arrhythmia deemed clinically related to the patients’ history of syncope, and the absence of an arrhythmia when the patient experienced syncope. Complications of ILR insertion were recorded. Given that normality was not assumed, a non-parametric analysis was utilised to compare baseline characteristics. Results A total of 215 patients were included in this study, of which 43 (20%) had a CIED inserted due to ILR findings. Baseline characteristics were similar between the groups except age (Table 1). 103 (47.9%) of the patients had a CSE (Figure 1). Symptomatic arrhythmias were the most common clinically significant events, out of which a sinus pause of greater than 6 seconds was most prevalent arrhythmia. There were no complications that required early explant of the ILR in this cohort. Conclusion ILR detected a clinically significant arrhythmia and the absence of an arrhythmia in 42.7% and 5.1% of patients with undetermined syncope, respectively. ILR use is a valuable diagnostic tool in this cohort of patients. Further studies looking at cost-effectiveness would be beneficial.Baseline characteristicsCSE and arrhythmias

Evidences of induced resistance in tomato against Alternaria solani: An investigation

Tomato is one of the major solanaceous vegetables, which has a unique place in the global vegetable market. Instead of being a high-value crop, there is still a need to do improvement in its potential against various biotic and abiotic stressors that adequately demolish its real yield. Alternaria solani (causing early blight disease) is designated as one of the fatal organisms that may reduce tomato crop yield by up to 80%. There were lots of methods, viz., chemical, cultural and biological suggested to overcome it. However, chemical strategies are much in vogue, but they have several negative consequences for human health and the ecosystem. Enlightening this issue, the efficacy of various treatments, viz., chemical fungicides (Amistar Top®, Nativo®, and Contaf®), biochar and fungal bioagent (Trichoderma viride) was assessed under both in vivo and in vitro conditions. Induced resistance is mediated by several regulating pathways, like salicylic acid and jasmonic acid. These mediating pathways manipulate different physiological processes like growth and development, stress tolerance, and defence mechanisms of the plant. The assessment of results revealed that among all treatments biochar at 3.25% by weight consistently displayed remarkable effectiveness against the early blight infection by triggering resistance and improving the overall performance of tomato plants. This result is attributed to improved soil health, fastening mineralization as well as absorption processes, and boosting the plant’s immunity with the use of a higher concentration of biochar. Hence, it could be recommended for the overall improvement of tomato crop and its sustainability.