Weather Variables Research Articles

Crop‐ and weather‐dependent yield and wind erosion benefits from a conservation practices system

AbstractWind erosion and variable weather challenge crop production in the northern Great Plains. Management that increases residue cover might mitigate wind erosion during the cash crop growing season. We evaluated horizontal sediment flux (modified Wilson and Cooke samplers) and cash crop yield across a single rotation of corn (Zea mays L.)–soybean (Glycine max (L.) Merr.)–spring wheat (Triticum aestivum L.) in paired fields with contrasting management. One field included cover crops and retained spring wheat straw (aspirational), while the other excluded both conservation practices over the 3‐year rotation (business‐as‐usual). Horizontal sediment flux rapidly decreased with days after cash crop planting (increasing crop canopy), regardless of management treatment. In 2 years (2020 corn and 2022 spring wheat), there was greater horizontal sediment flux, lower cash crop grain yield, and lower cash crop aboveground biomass in the aspirational versus business‐as‐usual field. In 2021 soybean, there was lower horizontal sediment flux, greater cash crop yield, and greater cash crop aboveground biomass in the aspirational versus business‐as‐usual field. Higher yield and lower horizontal sediment flux responses corresponded with the management treatment that produced the higher cash crop aboveground biomass. Additionally, our short‐term study indicated that in drought years, cover crops worsened the adverse effects of abnormally low precipitation on yield and biomass of 2020 corn but not 2021 soybean.

A durable, breathable, and weather-adaptive coating driven by particle self-assembly for radiative cooling and energy harvesting

The imperative to attain net-zero emissions emphasizes energy conservation. Radiative cooling stands out as a compelling technology in this pursuit for its self-sufficiency and cost-effectiveness. However, the radiative cooling faces the challenge in varied weather, including high ultraviolet (UV), cloudy and rainy days, primarily due to instability of radiative cooling materials and mono-energy conservation mechanism. To address this issue, a durable, breathable, and weather-adaptive coating (porous PTFE coating) has been developed through assembling polyfluortetraethylene (PTFE) nanoparticles enabled by the differential interaction in a binary-solvent system. The porous PTFE coating exhibits high solar reflectivity (94%) and thermal emissivity (93%), which results from the precisely tunable assembly of PTFE nanoparticles, forming a desired porous morphology. This design serves as effective scattering, achieving a sub-ambient cooling effect of approximately 5 ℃ at midday. With an outstanding UV protection factor (UPF) of 179.15, the porous PTFE coating sustained stability after 40 days exposure to solar radiation. Leveraging the porous PTFE coating's exceptional negative triboelectric effect, an engineered high-performance droplet electricity nanogenerator (DEG) achieves a notable power density of 153.8 mW/m2, revealing significant potential for raindrop energy harvesting on rainy days. The versatile porous PTFE coating, with its exceptional weather adaptation and UV stability, holds promise for diverse applications, advancing sustainable and efficient energy solutions with reliability in varying conditions.

Ground Electric Field, Atmospheric Weather and Electric Grid Variations in Northeast Greece Influenced by the March 2012 Solar Activity and the Moderate to Intense Geomagnetic Storms

In a recent paper, we extended a previous study on the solar solar influence to the generation of the March 2012 heatwave in the northeastern USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), Interplanetary space (ACE satellite), the Earth’s magnetosphere (Earth-based measurements, NOAA-19 satellite), the top of the clouds (Terra and Aqua satellites), and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather (rainfall, fast winds, decreased temperature) in Thrace started on 6 March 2012, the same day as the heatwave started in USA, (ii) during the March 2012 winter-like event in Thrace (6–15 March), the ACE satellite recorded enhanced fluxes of solar energetic particles (SEPs), while SOHO and PAMELA recorded solar protons at very high energies (>500 MeV), (iii) Between 3–31 March, the temperature in Alexandoupoli and the ACE/EPAM solar high energy (1.88–4.70 MeV) proton flux were strongly anticorelated (r = −0.75, p = 0.5). (iv) Thrace experienced particularly intense cyclonic circulation, during periods of magnetic storms on 8–10 and 12–13 March, which occurred after the arrival at ACE of two interplanetary shock waves, on March 8 and March 11, respectively, (v) at the beginning of the two above mentioned periods large atmospheric electric fields were recorded, with values ranging between ~−2000 V/m and ~1800 V/m on 8 March, (vi) the winter-like weather on 8–10 March 2012 occurred after the detection of the main SEP event related with a coronal mass ejection released in interplanetary space as a result of intense solar flare activity observed by SDO on 7 March 2012, (vi) the 8–10 March weather was related with a deep drop of ~63 °C in the cloud top temperature measured by MODIS/Terra, which favors strong precipitation. Finally, we analyzed data from the electric power network in Thrace (~41°N) and we found, for the first time sudden voltage changes of ~3.5 kV in the electric grid in Greece, during the decay phase of the March 2012 storm series. We discuss the winter-like March 2012 event in Thrace regarding the influence of solar cosmic rays on the low troposphere mediated by positive North Atlantic Oscillation (NAO). Finally, we infer that the novel finding of the geomagnetic effects on the electric power grid in Thrace may open a new window into space weather applications research.

Agronomic management factors impacting yield, quality stability, and environmental footprints of barley in a mediterranean environment

Context or Problems:The impact of different agronomic strategies (such as sowing, fertilization, and tillage) under different environmental conditions (soil type, and long-term weather conditions) can provide an increased knowledge on how barley grain yield, quality and environmental footprints can be optimized. Objectives:The objectives of this study are to: i) understand what are the different agronomic and environmental combinations that optimize grain quality and minimize environmental impacts; ii) which environmental-agronomic combination allow production and quality stability to be achieved over time, while providing lower environmental footprints. Methods:An existing crop simulation model was calibrated on a nitrogen response experiment and evaluated on an independent dataset of nitrogen response. Results:Overall, the interaction of sowing, soil type, fertilization and tillage impacted grain quality more than grain yield. The optimization of the environmental, economic and quality outcome is strongly soil-climate dependent to optimizing grain quality and minimizing environmental impacts. Rainfall in pre-sowing, vegetative and reproductive stages is the most important environmental parameter impacting grain yield. While grain quality needed for malting/distilling is impacted by interaction of weather and agronomic practices. ConclusionsIn Mediterranean environments, optimizing for quality, yield and lower environmental impacts is challenging, and crop simulations can help to provide useful information on the dynamic interactions of those factors. ImplicationsThe tradeoff indicator can be derived considering how agronomy-soil-weather interactions impact the relationship between net income and environmental implications (in terms of nitrogen losses). This indicator can be used for building a tool to be used with system-based models to evaluate at different spatial scales the impacts of different agronomic management (as impacted by weather variability) on economic and environmental sustainability.

Space weathering, grain size, and metamorphic heating effects on ordinary chondrite spectral reflectance parameters

AbstractThe exposure to irradiation from high‐energy particles alters the reflectance properties of asteroid surfaces and is referred to as space weathering. This process leads to an increase in spectral slope in visible and near‐infrared wavelengths. However, changes in the regolith particle size, which can vary dramatically among the asteroid population, are known to influence the spectral properties of meteorites and asteroids. In this context, we investigate the changes in spectral slope and absorption band depths of fresh and irradiated ordinary chondrite meteorites to quantitatively compare the effects of space weathering and grain size variations. To do so, we develop and employ the Spectral Analysis for Asteroid Reflectance Investigation routine that calculates the band parameters of reflectance spectra. We then formulate a parameter called the Space Weathering Index (SWI) that is designed to encapsulate spectral changes due to space weathering. We find that the SWI is strongly dependent on the spectral slope which complicates the interpretation of asteroid spectra in the context of grain size variations and space weathering. We also show that a second parameter, the Band Depth Index, is indicative of petrologic type. Finally, we use a linear discriminant analysis to classify asteroid reflectance spectra into H, L, LL, and unequilibrated ordinary chondrites.

Fast DC-link voltage control based on power flow management using linear ADRC combined with hybrid salp particle swarm algorithm for PV/wind energy conversion system

Hybrid Energy Systems (HESs) are offering several advantages over conventional generation systems because of the rising need for Renewable Energy Resources (RERs) and its high capability and flexibility of achieving the desired performance for both urban and rural areas. The main goals of HESs are to improve the system performance by increasing its flexibility, reliability and reducing generation instability. In the current work, a hybrid Photovoltaic/Wind Energy Conversion System (PV/WECS) is proposed to supply and support the utility grid. The generated power from the system is greatly responding to the change in both the wind speed and PV irradiance level, and as a result impacting the DC-Bus voltage of the system converters. Therefore, precise, fast and optimum controllers are required for the PV/WECS system to ensure the extraction and generation of stable maximum power during weather variations along with maintaining the DC-Link voltage at the desired value. Further, this study proposes a novel Linear Active Disturbance Rejection Control (LADRC) combined with a hybrid Salp Particle Swarm Algorithm (SPSA) to a grid-connected PV/WECS for extracting the Global Maximum Power (GMP), regulating the DC-Link voltage, and enhancing the active and reactive powers control. The capability of the proposed SPSA-LADRC based-MPPT approach is examined according to the tracking time, accuracy and response to internal and external disturbances and then comparing its performance with other techniques such as SPSA-SMC, SPSA-PID and SPSA-PI controllers respectively. The findings revealed that the LADRC can track and keep the DC-Link voltage as the desired reference value,1500 V, respond much faster than the PI, SMC, and PID controllers as well as keep the THD of the injected current into the grid at a low level of 2.25%. Finally, the performance of the proposed system is experimentally verified using Hardware-In-Loop (HIL). The obtained findings verify the capability and accuracy of the system and its great potential in the management of HESs.

Identification of reliable locations for wind power generation through a global analysis of wind droughts

Wind droughts, or prolonged periods of low wind speeds, pose challenges for electricity systems largely reliant on wind generation. Using weather reanalysis data, we analyzed the global distribution of and trends in wind droughts using an energy deficit metric that integrates the depth and duration of wind droughts. We identified regions with high power densities, low seasonal variability, and limited weather fluctuations that favor wind power generation, such as the American Midwest, Australia, the Sahara, Argentina, Central Asia, and Southern Africa. Northwestern Europe has high power densities but experiences more frequent and prolonged wind droughts due to higher weather variability. We found little evidence for strong trends in wind droughts over recent decades in most places. Rather, the most severe wind droughts in many places occurred before wind power substantially penetrated power systems, which suggests that historical weather data can be useful in designing reliable wind-reliant electricity systems.

Weather Based Forewarning Model for Powdery Mildew Disease of Mustard

Powdery mildew disease is the most important disease of mustard (Brassica Juncea) which is highly sensitive to weather condition particularly temperature and humidity. To ascertain the relationship between weather variables and powdery mildew disease of mustard crop at Sardarkrushinagar, weekly powdery mildew data of 12 years (2010-11 to 2021-22) was collected from Centre for Oilseed Research, S.D. Agricultural University, Sardarkrushinagar. The data were pooled over the year and correlated with corresponding weather parameters recorded in Agromet Observatory, S.D. Agricultural University, Sardarkrushinagar. The forewarning regression equation was developed with the help of weather variables to predict the disease incidence. The results revealed that the maximum and minimum temperature ranged 26.2 to 38.1oC and 8.7 to 18.5oC respectively found congenial for disease incidence. The weather variables like maximum temperature (Tmax) and minimum temperature (Tmin) correlated significantly positive with powdery mildew disease. While morning relative humidity (RH I) and evening relative humidity (RH II) correlated significantly negative with disease severity. The impact of rainfall (RF), sunshine hour (SSH) and wind speed (WS) found positive but non-significant on powdery mildew disease. The weather variables those have significant relationship with powdery mildew disease, were used to develop multiple regression model. The regression equation Y = 62.837 + 8.072*Tmax – 3.919*RH1 developed with maximum temperature and morning relative humidity caused up to 96 % variability in the powdery mildew disease. Hence the combined effect of Tmax and RH1 was more pronounced and most influencing weather parameters on powdery mildew disease. Therefore, model could be used for predicting the powdery mildew disease well in advance.

Changes in photovoltaic power output variability due to climate change in China: A multi-model ensemble mean analysis

Solar photovoltaic (PV) power plays a crucial role in mitigating climate change. However, climate change may amplify weather variability and extreme conditions. The extreme conditions can increase the very low PV output and thereby increase the need for grid stabilization services. This study examined how weather variability affects PV power output in the near- (2025–2054) and far-future (2071–2100). The ensemble mean calculated using seven global climate models participating in the coupled model intercomparison project phase 6 for three different shared socioeconomic pathways (SSPs) (SSP126, SSP245, SSP585) was used for the assessment. The standard deviation of the monthly PV power output and the share of very low monthly PV power output were used to assess the variability of PV power output. The findings indicate that the summer PV power output was projected to decrease by 6%–8% in central and northern Tibet under a high emissions scenario (SSP585). The summer months with low PV power output were projected to increase in western regions of China, known for its abundant solar resources. The findings of this study provide valuable insight for energy planners to make up for the influence of future weather variability.

Influence of Weather and Climate on Multidecadal Trends in Atlantic Hurricane Genesis and Tracks

Abstract This study investigates the relative roles of sea surface temperature–forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A 10-member initial condition ensemble with a ∼25-km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, although basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location. Significance Statement From the 1970s to the present, there has been an increase in the frequency of North Atlantic hurricanes, but they have also shifted in location to the east, away from land. We explore whether this shift in hurricanes’ locations was caused by climatic factors or randomness to understand if and how these trends will persist. We also consider whether the shift was due to a change in where hurricanes started or how they moved over their lifespan. Analyzing data from observed and simulated hurricanes, we find that the shift was made more likely by climate factors, but ultimately occurred due to random variability in the hurricanes’ starting locations. These results suggest a higher uncertainty in the future location and impact of hurricanes and highlight the importance of studying why hurricanes originate where they do.

Prediction of Severe Epidemics of Chickpea Ascochyta Blight Using Weather Variables

ABSTRACTChickpea production is threatened by severe epidemics of Ascochyta blight occurring in main chickpea growing lands under appropriate weather conditions worldwide. In this 4‐year research, occurrence of Ascochyta blight was monitored across nine main chickpea growing areas of Kermanshah province, western part of Iran. Each year, commercial chickpea fields were studied on a weekly basis from March to June. Disease data were collected as disease incidence (percentage of infected plants) and severity (percentage of infected tissues) and occurrence of epidemics. Weather data were collected as air temperature, rainfall, and relative humidity (RH) on a daily basis. According to a factor analysis, which explained 83% of data variance, 13 weather predictors were selected to estimate disease epidemics developed across different areas. Before modeling, a principal component analysis determined predictive values for these selected weather variables. Then, eight predictors of rainy days in March and April, mean RH in February, mean minimum temperature in January–March–April, and rainfalls in May and June were involved in model based on their predictive values. Current findings advanced our knowledge on the best weather predictors of severe epidemics of Ascochyta blight in chickpea crops at large scale.

Association of Short-term Increases in Ambient Fine Particulate Matter With Hospitalization for Asthma or COPD During Wildfire Season and Other Time Periods

BackgroundShort-term increases in air pollution are associated with poor asthma and chronic obstructive pulmonary disease (COPD) outcomes. Short-term elevations in fine particulate matter (PM2.5) due to wildfire smoke are becoming more common. Research QuestionThis study evaluated whether short-term increases in PM2.5 and ozone in wildfire season and in winter inversion season were associated with a composite of emergency or inpatient hospitalization for asthma and for COPD. Study Design and MethodsCase-crossover analyses evaluated 63,976 and 18,514 subjects hospitalized for primary discharge diagnoses of asthma and COPD, respectively, between January 1999 and March 2022. Subjects resided on Utah’s Wasatch Front where PM2.5 and ozone were measured by EPA-based monitors. Odds ratios (OR) were calculated using Poisson regression adjusted for weather variables. ResultsAsthma risk increased on the same day that PM2.5 increased during wildfire season (OR=1.057 per +10 μg/m3, CI: 1.019, 1.097, p=0.003) and winter inversions (OR=1.023 per +10 μg/m3, CI: 1.010, 1.037, p=0.0004). Risk decreased after 1 week, but during wildfire season risk rebounded at a 4-week lag (OR=1.098 per +10 μg/m3, CI: 1.033, 1.167). Asthma risk for adults during wildfire season was highest in the first 3 days after PM2.5 increases, but for children highest risk was delayed by 3-4 weeks. PM2.5 exposure was weakly associated with COPD hospitalization. Ozone exposure was not associated with elevated risks. InterpretationIn a large urban population, short-term increases in PM2.5 during wildfire season were associated with asthma hospitalization and the effect sizes were greater than for PM2.5 during inversion season.

THERMODYNAMIC MODEL OF STUDYING THE DYNAMICS OF THE TEMPERATURE BALANCE BY CALCULATING HEAT ENERGY IN AGRICULTURAL SECTOR

This work is the result of the simulation of a thermodynamic model that investigates the dy- namics of the temperature balance due to the transfer of thermal energy. A proposed scheme outlines the rheological heat transfer characteristics of an object featuring an insulated surface, along with accompany- ing graphs illustrating irreversible rheological transformations. The primary equation governing heat ex- change, incorporating a chemical reaction, is presented, and the equation dictating the rate of heat energy transfer along the object's length is derived. The subsequent step involves advancing physical- mathematical models to depict the conversion of thermal energy into various states of the object. This ad- vancement aims to facilitate an evaluation of the overall temperature regimen of agricultural entities and optimize the heating procedures. In recent years, the agricultural sector has faced substantial challenges attributed to climate change. Climate change impacts on agriculture encompass elevated temperatures, increased weather variability, shifting agroecosystem boundaries, invasive species, pests, and frequent ex- treme weather events. These changes possess the potential to disrupt agricultural yields and food security, underscoring the importance of comprehending and managing temperature dynamics within agricultural systems. This research delves into the core of temperature regulation within agricultural objects, employing a thermodynamic model that not only considers heat exchange but also incorporates the influence of chemical reactions. Through the derivation of equations characterizing the speed of heat energy transfer and the development of physical-mathematical models for thermal energy transformation, this study offers a robust framework for evaluating and refining the temperature regime of agricultural objects. This re- search paves the way for further advancements in the comprehension and management of temperature dynamics within agricultural systems. As climate change persists in posing challenges to the sector, the knowledge and models developed here will serve as invaluable tools in optimizing heating procedures, en- hancing agricultural resilience, and securing global food production. In summary, this study significantly contributes to our understanding of temperature balance and heat energy transfer within agricultural ob- jects. It represents a crucial stride towards addressing the challenges posed by climate change in agricul- ture.The suggested thermodynamic model, along with its corresponding equations, lays a robust ground- work for forthcoming investigations and practical implementations. Ultimately, this development stands to enhance the agricultural sector and contribute to global food production.

Border Defence Mechanism Using Deep Learning Technique

In response to escalating concerns regarding potential border airstrikes, this paper introduces an advanced deep-learning model that utilizes Convolutional Neural Networks (CNNs) for accurate military aircraft classification. Functioning as a pivotal tool for proactive threat assessment, the system works by distinguishing and categorizing six aircraft types with exceptional accuracy, facilitating real-time analysis, and aiding with the development of responsive counter-attack strategies. The multi-stage process begins with the comprehensive collection of diverse datasets, followed by the extraction of spatial features from images using CNNs. Optimization techniques fine-tune model parameters to ensure optimal performance. This approach significantly strengthens national security by leveraging advanced technology for proactive threat mitigation. Emphasizing this paper’s vital role in enhancing border defence capabilities, it highlights the model’s capacity to navigate evolving security challenges through improved awareness of the environment. The model's ability to adapt to real-world conditions, including variations in lighting, weather, and terrain, demonstrates its practical applicability. For seamless implementation, the deep learning model is deployed using Django and SQLite as a web page providing an efficient and user-friendly interface This sophisticated deep learning model is fundamental in fortifying national security measures against potential threats along border regions.

Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia.

The first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF's epidemiology in Saudi Arabia. In this study, we assess the impact of these variables on the DF burden of disease in Saudi Arabia and we attempt to create robust DF predictive models. Using 10 years of DF, weather, and pilgrimage data, we conducted a bivariate analysis investigating the role of weather and pilgrimage variables on DF incidence. We also compared the abilities of three different predictive models. Amongst weather variables, temperature and humidity had the strongest associations with DF incidence, while rainfall showed little to no significant relationship. Pilgrimage variables did not have strong associations with DF incidence. The random forest model had the highest predictive ability (R2 = 0.62) when previous DF data were withheld, and the ARIMA model was the best (R2 = 0.78) when previous DF data were incorporated. We found that a nonlinear machine-learning model incorporating temperature and humidity variables had the best prediction accuracy for DF, regardless of the availability of previous DF data. This finding can inform DF early warning systems and preparedness in Saudi Arabia.

Peramalan Cuaca Harian di Kota Purwokerto Menggunakan Metode Rantai Markov

Daily weather forecasting is an important aspect in anticipating and managing the impact of weather changes that can affect various sectors of life, such as agriculture, transportation, tourism, and other activities. Purwokerto City, which is located in the middle of Java Island, Indonesia, is one of the cities that faces fluctuating weather changes. This research aims to perform daily weather forecasting in Purwokerto City using the Markov Chain Method, a statistical approach that has been widely used in various fields including weather forecasting. This research uses daily weather data in Purwokerto City on July 5 to 10, 2023. The data was obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) Purwokerto City. The weather data is based on the weather categories of cloudy, light rain, moderate rain, and thunder rain, as well as observation time which includes morning, afternoon, evening, and night. The results showed that the daily weather data from July 5 to 10, 2023 analyzed showed significant weather variations. However, cloudy weather was the most dominant condition during that week.

Dietary flexibility of the greater bamboo lemur (Prolemur simus), a specialized feeder, in eastern Madagascar.

The degree of dietary flexibility in primates is species specific; some incorporate a wider array of resources than others. Extreme interannual weather variability in Madagascar results in seasonal resource scarcity which has been linked to specialized behaviors in lemurs. Prolemur simus, for example, has been considered an obligate specialist on large culm bamboo with >60% of its diet composed of woody bamboos requiring morphological and physiological adaptations to process. Recent studies reported an ever-expanding list of dietary items, suggesting that this species may not be an obligate specialist. However, long-term quantitative feeding data are unavailable across this species' range. To explore the dietary flexibility of P. simus, we collected data at two northern sites, Ambalafary and Sahavola, and one southern site, Vatovavy, from September 2010 to January 2016 and May 2017 to September 2018, respectively. In total, we recorded 4022 h of behavioral data using instantaneous sampling of adult males and females from one group in Ambalafary, and two groups each in Sahavola and Vatovavy. We recorded 45 plant species eaten by P. simus over 7 years. We also observed significant differences in seasonal dietary composition between study sites. In Ambalafary, bamboo was the most frequently observed resource consumed (92.2%); however, non-bamboo resources comprised nearly one-third of the diet of P. simus in Sahavola and over 60% in Vatovavy. Consumption of all bamboo resources increased during the dry season at Ambalafary and during the wet season at Vatovavy, but never exceeded non-bamboo feeding at the latter. Culm pith feeding was only observed at Ambalafary, where it was more common during the dry season. We identify P. simus as a bamboo facultative specialist capable of adjusting its feeding behavior to its environment, indicating greater dietary flexibility than previously documented, which may enable the species to survive in increasingly degraded habitats.

Seed priming with gas plasma-activated water in Ethiopia’s “orphan” crop tef (Eragrostis tef)

Main conclusionSeed priming with gas plasma-activated water results in an increased ageing resilience in Eragrostis tef grains compared to a conventional hydropriming protocol.Tef (Eragrostis tef) is a cereal grass and a major staple crop of Ethiopia and Eritrea. Despite its significant importance in terms of production, consumption, and cash crop value, tef has been understudied and its productivity is low. In this study, tef grains have undergone different priming treatments to enhance seed vigour and seedling performance. A conventional hydropriming and a novel additive priming technology with gas plasma-activated water (GPAW) have been used and tef grains were then subjected to germination performance assays and accelerated ageing. Tef priming increases the germination speed and vigour of the grains. Priming with GPAW retained the seed storage potential after ageing, therefore, presenting an innovative environmental-friendly seed technology with the prospect to address variable weather conditions and ultimately food insecurity. Seed technology opens new possibilities to increase productivity of tef crop farming to achieve a secure and resilient tef food system and economic growth in Ethiopia by sustainable intensification of agriculture beyond breeding.

Effect of Weather Parameters on Development of Powdery Mildew of Ber (Ziziphus mauritiana) Caused by Oidium erysiphoides

India’s one of the most popular fruits is the ber (Ziziphus mauritiana Lamk.). Ziziphus mauritiana is sometimes referred to as Indian plum or desert apple. Oidium erysiphoides f. sp. Ziziphi, which causes powdery mildew of ber, is more common and a threat in northern states. The white powdery coating on leaves and fruits affects the photosynthesis and growth & development, respectively. This disease results in 50-60% loss in fruit yield and reduces market value of the produce. In the present study, the effect of abiotic environmental elements such as temperature, relative humidity, wind speed, and others were observed and they had a significant impact on the progression of powdery mildew disease. In the study, which was carried out on two ber cultivars viz., Gola and Kaithali, the disease progression on both cultivars showed a significantly negative correlation with maximum (Tmax) and minimum temperature (Tmin) and a significantly positive correlation with morning (RHm) and evening relative humidity (RHe). The favourable weather variables were observed viz., Tmax above 20.3 °C, Tmin around 9.9 °C and RHm was more than 80%. The ber cultivar Gola was more susceptible to disease development than Kaithali, with Gola having the highest disease intensity.

An optimization and discrete event simulation framework for evaluating delivery performance in Swedish wood supply chains under stochastic weather variations

ABSTRACT Delivery performance in wood supply chains is affected by carrying capacity on forest roads. Stochastic weather variations affect carrying capacity of roads and accessibility of harvested wood on landings. In Sweden, annual harvest scheduling is usually based on four seasons and road accessibility classes are set to match these seasons. In reality, road accessibility changes depend on weather variations not season. This causes a risk of poor delivery performance. Delivery performance is evaluated using two performance indicators, backorders (orders not delivered on time) and lead time (time from harvesting to delivery to mill). A data-analytic framework, combining optimization and simulation, is proposed to assess and evaluate these performance indicators for the wood supply chain under stochastic weather variations. An optimization model is used to generate a feasible harvest schedule for expected weather. This is thereafter evaluated given various weather scenarios using a discrete-event simulation model. The combination of optimization and discrete-event simulation is used to assess delivery performance over time, and changes in road accessibility caused by weather variation. An expected decrease in road accessibility implies a stable delivery performance, while an unexpected decrease causes a reduced delivery performance.