Variable Climatic Conditions Research Articles

Analysis of recent decade rainfall data with new exponential-exponential distribution: Inference and applications

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management.

Real time performance assessment of utility grid interfaced solar photovoltaic plant

Continuous monitoring of large-scale solar photovoltaic (PV) installations is necessary to check the deterioration and monitor the performance of the PV plant. Fault diagnosis is crucial to ensure the PV plant operates safely and reliably. This paper presents a diagnosis methodology based on current-voltage (I-V) and PV characteristics to monitor and assess the behavior of solar PV. In this paper, I-V curve characterization using an I-V curve tracer is used to check the deterioration and diagnosis of the PV panels. The real-time performance of the 50.4 kWp rooftop solar grid interfaced PV plant is investigated and analyzed using I-V and PV curve tracers in real-time conditions. The overall performance of solar PV is assessed on a real-time test system in different scenarios such as variable climatic conditions, partial shading conditions, aging of solar panels, short circuit conditions, and dust decomposition. Furthermore, the performance assessment of solar PV is evaluated using performance indicators such as open circuit voltage index, short circuit current index, fill factor, and performance ratio.

EXPLORING THE MICRO AND MACRO TERROIR OF FETEASCĂ NEAGRĂ WINE FROM MOLDOVA

This study delves into the intricate realm of Moldovan winemaking, focusing on the iconic Fetească Neagră grape variety. The research highlights the importance of indigenous grape varieties in shaping a unique wine identity for the region. Drawing on the concept of terroir, the study investigates the microbial terroir specific to three designated geographical regions in Moldova during the 2022 vintage. The research employs a multifaceted approach, combining climate data analysis, mini vinification experiments, microbial DNA extraction, and sensory evaluation techniques. Results reveal significant variations in climatic conditions and microbial diversity across the three regions, influencing the sensory characteristics of the resulting wines. On vintage 2022 the Codru is temperature， and the Stefan-Voda and Vadul lui Traian are warm climates. All the 3 regions are moderately dry. The Stefan Voda is 14.0%， and manifests the highest ethanol content among the regions, alongside the lowest pH level. The sensory evaluations show Stefan Voda evincing heightened creaminess, Codru showcasing prominent herbal nuances, and Vadul lui Traian displaying elevated scores in berry, cherry, violet, and black pepper descriptors. Through meticulous analysis, the study underscores the dynamic interplay between environmental factors, microbial communities, and wine attributes, shedding light on the complex tapestry of Moldovan winemaking. Ultimately, this research contributes to a deeper understanding of terroir-driven wine production and emphasizes the importance of preserving indigenous grape varieties to uphold the distinctiveness of Moldovan wines on the global stage.

CSM-CROPGRO model to simulate safflower phenological development and yield.

Crop simulation models are valuable tools for decision making regarding evaluation and crop improvement under different field conditions. CSM-CROPGRO model integrates genotype, environment and crop management portfolios to simulate growth, development and yield. Modeling the safflower response to varied climate regimes are needed to strengthen its productivity dynamics. The main objective of the study was to evaluate the performance of DSSAT-CSM-CROPGRO-Safflower (Version 4.8.2) under diverse climatic conditions. The model was calibrated using the field observations for phenology, biomass and safflower grain yield (SGY) of the year 2016-17. Estimation of genetic coefficients was performed using GLUE (Genetic Likelihood Uncertainty Estimation) program. Simulated results for days to flowering, maturity, biomass at flowering and maturity and SGY were predicted reasonably with good statistical indices. Model evaluation results elucidate phenological events with low root mean square error (6.32 and 6.52) and high d-index (0.95 and 0.96) for days to flowering and maturity respectively for all genotypes and climate conditions. Fair prediction of safflower biomass at flowering and maturity showed low RMSE (887.3 and 564.3kgha-1) and high d-index (0.67 and 0.93) for the studied genotypes across the environments. RMSE for validated safflower grain yield (101.8kgha-1) and d-index (0.95) depicted that model outperformed for all genotypes and growing conditions. Longer appropriate growing conditions at NARC-Islamabad took optimal duration to assimilate photosynthetic products lead to higher grain yield. Safflower resilience to different environments showed that it can be used as an alternate crop for different agroecological regions. Furthermore, CROPGRO-Safflower model can be used as tool to further evaluate inclusion of safflower in the existing cropping systems of studied regions.

Sustainable and self-sufficient social home through a combined PV‑hydrogen pilot

Utilizing surplus renewable energy to produce and store green hydrogen can help meet energy demands during periods of low renewable energy production, and thus contributing greatly to decarbonize emission-intensive sectors, such as the residential and building sector. In this context, this manuscript reports the design, construction and operation of a hybrid PV‑hydrogen demonstrative pilot (PVHyP) for the 100% electrical self-sufficiency of a social housing in the town Novales (Spain). The work addresses i) the collection of real consumption data from the dwelling, ii) the design considering real characteristics and market availability of the devices, iii) final balance of plant implementation, and iv) the energy management strategy followed. The implemented control system allows the facility to be tracked and operated remotely. Besides, this flagship implementation in the SUDOE region (Spain, Portugal and southwest of France) has achieved the electrical independence from the grid under diverse climate conditions, eliminating 2260 kg of CO2 emissions associated to electricity consumption, saving almost 15,200 kWh of primary energy during two years of monitoring. Thus, around 1170 € where avoided in electricity bills (100% savings), resulting on multiple benefits for social dwellers in risk of energy poverty.

Ancient prairies as a reference for soil organic carbon content and microbial community structure

The historic remnant prairies of Missouri display diversity above and below the soil surface and represent the few remaining acres of unconverted grassland habitat in the state. The soil microbiome is foundational to nutrient acquisition, resource partitioning, and ecosystem resilience; therefore, the intact plant-soil interactions of remnant prairies can serve as a reference for microbial community structure and ecosystem services lost due to land conversion. This research was conducted to show the importance of reference soil conditions and the variability that lies therein while contrasting degraded soil ecosystems to provide a standard to which comparable soils should be held. In this study, 35 sites across Missouri representing three prairie management conditions on similar soils and variable climatic conditions were compared to assess differences in the microbial community structure and biomass using phospholipid fatty acid (PLFA) analysis. Surface (0–7.5 cm) soil organic carbon (SOC) content provided a measure of substrate available to support microbial community health. Total microbial biomass, the biomass of individual microbial groups, selected biomarker ratios, and SOC, were significantly different across treatments and highest in remnant prairies apart from Saprophytic fungi and eukaryotes. This study demonstrated that the soil microbial community in reconstructed and restored prairie systems remained significantly diminished in biomass relative to remnant prairies, even after several decades. This study highlights the need to preserve remnant prairies, where soil is in its native state under native flora and abiotic conditions. These results also illustrate the importance of selecting remnant systems as the ecological reference standard for prairie lands.

Risk-based hydrologic design under climate change using stochastic weather and watershed modeling

Water resources planning and management requires the estimation of extreme design events. Anticipated climate change is playing an increasingly prominent role in the planning and design of long-lived infrastructure, as changes to climate forcings are expected to alter the distribution of extremes in ways and to extents that are difficult to predict. One approach is to use climate projections to force hydrologic models, but this raises two challenges. First, global climate models generally focus on much larger scales than are relevant to hydrologic design, and regional climate models that better capture small scale dynamics are too computationally expensive for large ensemble analyses. Second, hydrologic models systematically misrepresent the variance and higher moments of streamflow response to climate, resulting in a mischaracterization of the extreme flows of most interest. To address both issues, we propose a new framework for non-stationary risk-based hydrologic design that combines a stochastic weather generator (SWG) that accurately replicates basin-scale weather and a stochastic watershed model (SWM) that accurately represents the distribution of extreme flows. The joint SWG-SWM framework can generate large ensembles of future hydrologic simulations under varying climate conditions, from which design statistics and their uncertainties can be estimated. The SWG-SWM framework is demonstrated for the Squannacook River in the Northeast United States. Standard approaches to design flows, like the T-year flood, are difficult to interpret under non-stationarity, but the SWG-SWM simulations can readily be adapted to risk and reliability metrics which bare the same interpretation under stationary and non-stationary conditions. As an example, we provide an analysis comparing the use of risk and more traditional T-year design events, and conclude that risk-based metrics have the potential to reduce regret of over- and under-design compared to traditional return-period based analyses.

Energetic analysis and economic viability of active atmospheric water generation technologies

Water scarcity is a growing global and systematic problem in regions with low groundwater availability. Atmospheric water generation (AWG) technologies are an innovative solution to the water shortage problem, as atmospheric water vapor is a readily available resource even in arid regions, with the drawback of high energy consumption. In this paper, the viability of AWG technologies on an energy and economic level is investigated by thermodynamic modeling of three main active AWG systems consisting of cooling condensation, adsorption and absorption processes. A location analysis model is developed to evaluate the performance based on representative weather data of temperature, pressure and relative humidity over a period of one year to account for seasonal shifts and daily variations in climatic conditions. The specific energy consumption kWh/kg, water production trend and total specific cost are calculated for each technology. Water production by seawater desalination at the nearest coastline and transportation to the site by tanker truck, as well as bottled water prices, are used as benchmarks to assess economic viability. The results show that active AWG systems can only be an economically viable alternative if the water consumption site is relatively far from the coast or other water-rich regions and low electricity costs are available (distance >600 km, electricity price <0.10 US$/kWh). Compared to bottled water, all AWG technologies are in a competitive price range. Absorption systems have an energy efficiency advantage over conventional cooling condensation and adsorption systems (cooling condensation: average 0.42 kWh/kg; absorption: average 0.38 kWh/kg; adsorption: average 1.16 kWh/kg), but require a higher degree of process and plant design development. However, because of the high fluctuation in water production, atmospheric water generation technologies should be considered as a complementary supply to conventional water sources.

Disease Reaction Studies for Turcicum Leaf Blight (Exserohilum Turcicum) Disease in Maize (Zea Mays L.) Under Artificial Epiphytotic Conditions

Maize is widely cultivated around the globe under varied climatic conditions. India is one of the major maize producing countries and stands fourth in terms of production. Turcicum leaf blight (TLB) disease caused by the pathogen, Exserohilum turcicum is one of the major diseases of maize causing varying extent of grain yield loss from 15 to 30%. To overcome the losses due to the disease, resistant cultivars needs to be developed. Hence, the identification of resistant sources is important in order to develop TLB resistant hybrids. Present investigation was conducted to identify TLB resistant inbreds and hybrids. Simplified Triple Test Cross (STTC) design was employed to develop 96 hybrids using twenty inbred lines. Further the hybrids and inbred lines along with two checks (Resistant –NAI 137; Susceptible – CM 202) were evaluated for TLB disease reaction under artificial epiphytotic condition during rabi 2019 in simple lattice design with two replications. Scoring was done from 60 to 80 days after sowing based on 1-9 TLB disease scale. Among the inbred lines V-86, MAI-295, V-70, MAI-214, V-85, 40375, V-72, V-39, 40061 were resistant and MAI-288, V-24 were susceptible. Among hybrids, 36 were resistant, 44 were moderately resistant. 14 were moderately susceptible and two hybrids were susceptible to TLB disease. Identified TLB resistant hybrids needs to be further evaluated to test their performance and stability across locations. These resistant sources can be used to transfer TLB disease resistance.

Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models

Hydrological modelling can be complex in nonhomogeneous catchments with diverse geological, climatic, and topographic conditions. In this study, an integrated conceptual model including the snow module with machine learning modelling approaches was implemented for daily rainfall-runoff modelling in mostly karst Ljubljanica catchment, Slovenia, which has heterogeneous characteristics and is potentially exposed to extreme events that make the modelling process more challenging and crucial. In this regard, the conceptual model CemaNeige Génie Rural à 6 paramètres Journalier (CemaNeige GR6J) was combined with machine learning models, namely wavelet-based support vector regression (WSVR) and wavelet-based multivariate adaptive regression spline (WMARS) to enhance modelling performance. In this study, the performance of the models was comprehensively investigated, considering their ability to forecast daily extreme runoff. Although CemaNeige GR6J yielded a very good performance, it overestimated low flows. The WSVR and WMARS models yielded poorer performance than the conceptual and hybrid models. The hybrid model approach improved the performance of the machine learning models and the conceptual model by revealing the linkage between variables and runoff in the conceptual model, which provided more accurate results for extreme flows. Accordingly, the hybrid models improved the forecasting performance of the maximum flows up to 40 % and 61 %, and minimum flows up to 73 % and 72 % compared to the CemaNeige GR6J and stand-alone machine learning models. In this regard, the hybrid model approach can enhance the daily rainfall-runoff modelling performance in nonhomogeneous and karst catchments where the hydrological process can be more complicated.

Effect of silicon nanoparticle-based biochar on wheat growth, antioxidants and nutrients concentration under salinity stress

Globally, salinity is an important abiotic stress in agriculture. It induced oxidative stress and nutritional imbalance in plants, resulting in poor crop productivity. Applying silicon (Si) can improve the uptake of macronutrients. On the other hand, using biochar as a soil amendment can also decrease salinity stress due to its high porosity, cation exchange capacity, and water-holding capacity. That’s why the current experiment was conducted with novelty to explore the impact of silicon nanoparticle-based biochar (Si-BC) on wheat cultivated on salt-affected soil. There were 3 levels of Si-BC, i.e., control (0), 1% Si-BC1, and 2.5% Si-BC2 applied in 3 replicates under 0 and 200 mM NaCl following a completely randomized design. Results showed that treatment 2.5% Si-BC2 performed significantly better for the enhancement in shoot and root length, shoot and root fresh weight, shoot and root dry weight, number of leaves, number of tillers, number of spikelets, spike length, spike fresh and dry weight compared to control under no stress and salinity stress (200 mM NaCl). A significant enhancement in chlorophyll a (~ 18%), chlorophyll b (~ 22%), total chlorophyll (~ 20%), carotenoid (~ 60%), relative water contents (~ 58%) also signified the effectiveness of treatment 2.5% Si-BC2 than control under 200 mM NaCl. In conclusion, treatment 2.5% Si-BC2 can potentially mitigate the salinity stress in wheat by regulating antioxidants and improving N, K concentration, and gas exchange attributes while decreasing Na and Cl concentration and electrolyte leakage. More investigations at the field level are recommended for the declaration of treatment 2.5% Si-BC2 as the best amendment for alleviating salinity stress in different crops under variable climatic conditions.

Microbial Biotechnology: A Key Tool for Addressing Climate Change and Food Insecurity

Amidst escalating climate change and food insecurity concerns, exploring the potential of microbes offers a promising and sustainable solution. This review delves into the complex interplay between microbial communities and the dual challenge of environmental crisis and food security.&#x0D; Ubiquitous microorganisms – from bacteria to fungi and archaea – shape our planet's ecosystems, playing a crucial role in soil health, nutrient cycling, and plant-microbe interactions. This review dissects diverse microbial habitats, highlighting their remarkable adaptability to varied environments.&#x0D; It then underscores the reciprocal impacts of human-induced environmental changes on microbes and their habitats. Addressing these challenges, the review presents microbes as powerful allies in mitigating climate change. Their ability to sequester carbon, reduce greenhouse gas emissions, and enhance soil fertility is explored. Innovations like biofertilizers and biopesticides demonstrate the potential of microbial technologies to revolutionize agriculture and ensure global food security.&#x0D; Concluding, the review emphasizes the symbiotic link between microbes and sustainable food production. Microbial technologies can adapt agriculture to changing climate conditions, addressing water scarcity and enhancing soil moisture retention. Their potential to boost productivity in both traditional and precision agriculture under diverse climatic conditions is highlighted.&#x0D; This review calls for the urgent recognition and harnessing of microbial power for a sustainable future. Embracing microbial technologies not only fosters environmental stewardship but also paves the way for a resilient and resource-efficient agricultural future.

Thermodynamic Performance Assessment of Air Conditioner Combining Evaporative and Passive Cooling

Abstract The global increase in refrigeration and air conditioning applications poses a severe problem as regards the environmental degradation caused by greenhouse gas emissions. This study introduces a novel approach wherein both evaporative cooling and passive cooling are integrated to unveil notable enhancements in energy and exergy compared to conventional air conditioning systems. Therefore, this work aims to enhance the thermal performance of a 1.5-ton split air conditioner (SAC) employing outdoor (condenser) evaporative and indoor passive cooling (EPC). The heat removal capacity of a condenser and SAC performance are greatly affected by the air temperature at the condenser inlet. Evaporative cooling serves to lower outdoor air temperature, while passive cooling minimizes the indoor cooling load. Design parameters encompass outdoor temperature (Ta = 30–44 °C), relative humidity (RH = 20–80%), and temperature reduction due to passive cooling (ΔTR = 0.5–5 °C). A model is developed to calculate the temperature reductions of outdoor air through evaporative cooling in diverse climatic conditions, while the range of passive cooling degrees is obtained from previous experiments. Results indicate a substantial enhancement in the thermodynamic performance of the proposed system. The maximum coefficient of performance (COP) improvement of 68.66% is achieved at 44 °C outside temperature and 20% relative humidity. Annual energy savings, under extreme operating conditions, range from 358.4 kWh to 2116.8 kWh. The EPC SAC is identified as more sustainable than the conventional split air conditioner (CSAC). Moreover, the projected system is anticipated to recoup its costs within a relatively short period of 1.42 years.

Evaluating the Performance of Potato Genotypes Across Diverse Climatic Conditions

Potato (&amp;lt;i&amp;gt;Solanum tuberosum&amp;lt;/i&amp;gt; L.) is a globally important crop plant producing high yields of nutritionally valuable food in the form of tubers. Growing conditions across Pakistan are not uniform and it is important to dissect the genotype by environment interaction to identify the material suited to a particular environment. In this study, fifteen potato genotypes, namely FD49-28, FD3-10, FD1-8, SH-19, SH-90, SH-330, SH-80, SH-95, SH-216A, and SH-103, along with standard varieties SH-5, Cardinal, and Diamant, were evaluated for their adaptability across four different locations in Pakistan during the autumn season of 2019-20 and typical agronomic practices were performed and a number of parameters including tuber yield were assessed. Results for tuber grades indicated that the genotypes FD3-10, SH-5, and SH-216A demonstrated better performance. Furthermore, the genotypes FD8-1 and FD1-10 showed a higher level of tolerance to tuber diseases, indicating their potential for disease resistance in potato cultivation. Regarding tuber yield, SH-216A and FD49-28 were the top-performing genotypes, achieving significantly higher yields of 27 t/ha and 26.2 t/ha, respectively, when compared to the other varieties, including the standard ones. The material evaluated and information provided can be used to improve production methods for the industry.

Shape optimization of the energy efficiency of building retrofitted facade

The current state of research indicates a necessity of further examination in both numerical and experimental studies related to optimizing shapes of building enclosures for the enhancement of their energy efficiency. The demand for research primarily arises due to the numerical complexities associated with optimizing shapes for this specific purpose. Consequently, the primary objective of this article is to address and bridge these gaps in the field. To achieve this, a two-dimensional steady-state heat diffusion model is assumed to represent the physical processes occurring within building facades of varying shapes. A third type boundary condition is applied to the exterior boundary, encompassing convective and incident short-wave solar radiation effects. The calculation of short-wave radiation accounts for factors such as sunlight exposure and shading, influenced by the surrounding urban environment. The internal boundary interfaces with the indoor ambient air, and thus, a Robin boundary condition is adopted. To tackle the computational demands while ensuring accuracy, the boundary element method (BEM) is employed by discretizing the domain boundary into discrete elements. Then, two heat transfer design objectives are define according to the period of investigations: ones related to enhancing heat transfer and ones focused on thermal insulation problem. Last, a real-world case study is conducted, considering a house wall under varying climate conditions throughout the year. Optimal shapes for the external wall boundary are determined with the constraint that the optimized facade utilizes the same amount of material as the reference flat one. The results demonstrate a substantial increase in energy efficiency compared to the reference flat wall case.

Climate changes altered the citrus fruit quality: A 9-year case study in China

Global climate change has significantly impacted the production of various crops, particularly long-term fruit-bearing plants such as citrus. This study analyzed the fruit quality of 12 citrus orchards (Citrus Sinensis L.Osbeck cv. Bingtang) in a subtropical region in Yunnan, China from 2014 to 2022. The results indicated that high rainfall (>220 mm) and low cumulative temperature (<3150 °C) promoted increases in titratable acidity (>1.8 %) in young fruits. As the fruits further expanded (with a horizontal diameter increasing from 50 to 65 mm), excessive rainfall (300-400 mm), lower cumulative temperature (<2400 °C), and a reduced diurnal temperature range (<10 °C) hindered decreases in titratable acidity. Conversely, low rainfall (<220 mm), high cumulative temperature (>3150 °C), and a high diurnal temperature range (>14 °C) promoted the accumulation of soluble solids in young fruits (9 %) at 120 days after flowering (DAF). Furthermore, low rainfall (<100 mm) favored the accumulation of soluble solids (1.5 %) during fruit expansion (195-225DAF). To quantify the relationship between fruit acidity and climate variables at 120 DAF, we developed a regression model, which was further validated by actual measurements and accurately predicted fruit acidity in 2023. Our findings have the potential to assist citrus growers in optimizing cultivation techniques for the production of high-quality citrus under increasingly variable climatic conditions.

Global evapotranspiration simulation research using a coupled deep learning algorithm with physical mechanisms

AbstractEvapotranspiration (ET) and actual evapotranspiration (AET) serve as critical parameters in the water vapour exchange between terrestrial surfaces and the atmosphere. ET denotes the theoretical maximum evapotranspiration achievable under ideal conditions, whereas AET represents the actual evapotranspiration observed, factoring in the constraints imposed by available water resources. Precise estimation of AET is imperative for the optimization of water resource management and the advancement of sustainable development initiatives. In recent years, deep learning techniques have been extensively utilized in AET estimation. However, traditional deep learning models often lack the incorporation of essential physical constraints. We proceeded to enhance the loss function of the temporal convolutional network (TCN) by taking into account the physical relationships that exist among soil water content (SWC), potential evapotranspiration (PET) and AET, thereby introducing a novel physically coupled deep learning model (AET, SWC after kernel principal component analysis, PET, TCN and AKP‐TCN), and checked the rationality of the model with the FLUXNET 2015 dataset. These findings underscore that the AKP‐TCN model exhibits heightened sensitivity to peak fluctuations in AET under the imposition of physical constraints. This approach notably enhances the precision of AET simulations in areas marked by complex and variable climatic conditions, such as the Mediterranean climate zone and Oceania, achieving determination coefficient (R2) values surpassing the threshold of 0.900. Compared to traditional models, which include long short‐term memory (LSTM), convolutional neural networks (CNN) and TCN, the AKP‐TCN delivers substantial R2 improvements of 16%, 16% and 9%, respectively. This advancement offers a novel perspective for coupling deep learning with physical mechanisms.

Identifying Spatial Determinants of Rice Yields in Main Producing Areas of China Using Geospatial Machine Learning

Rice yield is essential to global food security under increasingly frequent and severe climate change events. Spatial analysis of rice yields becomes more critical for regional action to ensure yields and reduce climate impacts. However, the understanding of the spatially varied geographical, climate, soil, and environmental factors of rice yields needs to be improved, leading to potentially biased local rice yield prediction and responses to climate change. This study develops a spatial machine learning-based approach that integrates machine learning and spatial stratified heterogeneity models to identify the determinants and spatial interactions of rice yields in the main rice-producing areas of China, the world’s largest rice-producing nation. A series of satellite remote sensing-derived variables are collected to characterize varied geographical, climate, soil, and environmental conditions and explain the spatial disparities of rice yields. The first step is to explore the spatial clustering patterns of the rice yield distributions using spatially global and local autocorrelation models. Next, a Geographically Optimal Zones-based Heterogeneity (GOZH) model, which integrates spatial stratified heterogeneity models and machine learning, is employed to explore the power of determinants (PD) of individual spatial variables in influencing the spatial disparities of rice yields. Third, geographically optimal zones are identified with the machine learning-derived optimal spatial overlay of multiple geographical variables. Finally, the overall PD of various variables affecting rice yield distributions is calculated using the multiple variables-determined geographically optimal zones and the GOZH model. The comparison between the developed spatial machine learning-based approach and previous related models demonstrates that the GOZH model is an effective and robust approach for identifying the spatial determinants and their spatial interactions with rice yields. The identified spatial determinants and their interactions are essential for enhancing regional agricultural management practices and optimizing resource allocation within diverse main rice-producing regions. The comprehensive understanding of the spatial determinants and heterogeneity of rice yields of this study has a broad impact on agricultural strategies and food security.

Interaction of Zinc Mineral Nutrition and Plant Growth-Promoting Bacteria in Tropical Agricultural Systems: A Review.

The relationship between zinc mineral nutrition and plant growth-promoting bacteria (PGPB) is pivotal in enhancing agricultural productivity, especially in tropical regions characterized by diverse climatic conditions and soil variability. This review synthesizes and critically evaluates current knowledge regarding the synergistic interaction between zinc mineral nutrition and PGPB in tropical agricultural systems. Zinc is an essential and fundamental micronutrient for various physiological and biochemical processes in plants. Its deficiency affects plant growth and development, decreasing yields and nutritional quality. In tropical regions, where soil zinc availability is often limited or imbalanced, the PGPB, through different mechanisms such as Zn solubilization; siderophore production; and phytohormone synthesis, supports Zn uptake and assimilation, thereby facilitating the adverse effects of zinc deficiency in plants. This review outlines the impacts of Zn-PGPB interactions on plant growth, root architecture, and productivity in tropical agricultural systems. The positive relationship between PGPB and plants facilitates Zn uptake and improves nutrient use efficiency, overall crop performance, and agronomic biofortification. In addition, this review highlights the importance of considering indigenous PGPB strains for specific tropical agroecosystems, acknowledging their adaptability to local conditions and their potential in sustainable agricultural practices. It is concluded that Zn fertilizer and PGPBs have synergistic interactions and can offer promising avenues for sustainable agriculture, addressing nutritional deficiencies, improving crop resilience, and ensuring food security.

The Colouration in Dicrurus caerulescens (White-bellied Drongo) Follows the Precipitation and Temperature of the Environment

The variation in climatic conditions over space and time is considered a major driving force in speciation. Gloger's rule is one such rule that broadly explains the variation in the colouration of endotherms (birds and mammals) with the effects of climatic parameters. This predicts that endothermic animals tend to have darker colouration in warm and rainy climates. Here we have tried to explain the variation in the belly white colour in Dicrurus caerulescens (White-bellied Drongo), an overall black coloured bird with a variable white belly. The dark bellied birds are considered the subspecies D. c., and the pale bellied birds are considered D. c.insularis (in dryzone of Sri Lanka). A total number of 112 individuals of adult White-bellied Drongo, including field (n=23) and museum samples (n=36) from Sri Lanka (Total 59), and museum samples from India (n=45) and Nepal (n=8) which were deposited at the National Museum of Sri Lanka, Field Museum of Natural History, USA and the Natural History Museum, UK were taken into consideration. The length of the white belly was measured using a dial calliper adopting a standardized method to minimize inconsistencies. To understand how the extent of white belly varied with respect to precipitation and temperature, we adopted generalized least squares (GLS) methods accounting for spatial autocorrelation between data points. The climatic variables were extracted from the world climatic data through QGIS software. The GLS methods were carried out using the ―nlme‖ package in the R Software for different orders of Autoregressive-moving average (ARMA) for the squared value of white belly length. To find the best fitting model, we used the Akaike information criterion (AIC) using the package ―AICcmodavg‖ in the R. From this, we found a negative significant effect of the interaction between the temperature and precipitation for the white belly length over the studied area in ARMA (1, 1) correlation (-3.72e-01±-0.022, t= -2.23, p&lt;0.05). The white belly is smaller; hence the birds are darker where there is higher temperature and precipitation. The reverse (pale belly) is true when lower temperatures and/or humidity. Therefore, this follows the Gloger's rule of becoming darker in colouration with the high rainfall and temperature in D. caerulescens. &#x0D; Keywords: Dicrurus caerulescens, Colouration, Gloger's rule, Temperature, Precipitation