Journal of Agrometeorology

Relationship between paddy cultivation and methane emission rate (XCH4) in Haryana

2024-06-10T21:59:42+00:00

Application of discriminant function analysis for forecasting wheat yield in Jaunpur district, Uttar Pradesh

2024-08-01T16:55:57+00:00

Impact of climatic factors on rice production in Bankura district of West Bengal, India

2024-08-11T01:11:41+00:00

Use of machine learning techniques in predicting inflow in Tarbela reservoir of Upper Indus Basin

2024-08-07T00:10:17+00:00

Sugarcane acreage estimation using satellite imagery and machine learning

2024-08-28T03:40:49+00:00

Daily rainfall prediction using long short-term memory (LSTM) algorithm

2024-09-14T14:47:14+00:00

Brown patch severity of lawn grass species as influenced by weather parameters in mid-hills of Himachal Pradesh

2024-09-06T01:10:13+00:00

Trends in rainfall and temperature extremes during 1954-2019 in Addis Ababa, Ethiopia

2024-09-18T03:58:50+00:00

Optimizing irrigation water requirements of drip-irrigated spring/summer vegetable crops in Jalandhar

2024-10-03T08:41:14+00:00

Thermal utilization of greengram (Vigna radiate L. Wilczek) under different sowing dates and nutrient managements

2024-10-05T00:41:14+00:00

Climate-smart agriculture in India: Greenhouse gas mitigation strategies

2024-10-05T06:42:16+00:00

This review paper examines Climate-Smart Agriculture (CSA) as a crucial approach to mitigate greenhouse gas emissions from India's agriculture sector. It analyzes various CSA practices implemented in India, focusing on their effectiveness in reducing emissions while enhancing food security and farmer livelihoods. The paper explores crop management techniques like improved varieties, nutrient management, and water management, alongside soil management practices such as conservation agriculture and agroforestry. Additionally, it delves into livestock management strategies, including improved feeding practices and manure management. The review highlights the role of government policies and programs in promoting CSA adoption, such as the National Mission for Sustainable Agriculture and the Parampara at Krishi Vikas Yojana. Challenges hindering wider CSA adoption, including financial constraints, lack of awareness, and data gaps, are discussed. The paper concludes by emphasizing the need to address these challenges and leverage opportunities like strengthening extension services, promoting farmer-to-farmer learning, and utilizing technology to unlock the full potential of CSA in India.

Evaluating rice crop phenology and crop yield in hilly region using satellite imagery and Google Earth Engine

2024-07-30T12:44:17+00:00

Monitoring vegetation phenology is essential for understanding the impacts of climate change on agricultural production. This study leverages Sentinel-2 data to develop an algorithm in Google Earth Engine (GEE) for calculating phenological metrics of rice crop cultivated over the hilly area, allowing for high-resolution, efficient, and large-scale analysis without the need for data download. The study focuses on key metrics, including the start of the season and end of the season , length of growing season derived from various vegetation indices. The results demonstrate that NDVI-based phenological metrics closely align with the observed values at the experimental site, Malan. Moreover, the relationship of NDVI based length of growing season with the rice crop yield was found stronger with a R² value of 0.68, depicting the capability of the satellite-based phenology metrics to estimate the rice crop yield in hilly region of Himachal Pradesh.

Comprehensive air quality analysis in Karbala: Investigating the relationships between meteorological factors and pollutants across different landscapes

2024-07-26T03:45:50+00:00

Atmospheric elements interact with pollutants in complex, multidimensional ways, affecting air quality. Understanding these relationships requires a comprehensive analysis of time-series weather and pollutant data which has a negative or positive impact on the ecosystem and human health. This study examines the relationships between meteorological factors and air pollutants in Karbala, Iraq, using data from January 2021 to June 2024. Principal component analysis (PCA) revealed that photochemical smog (45-46%), particulate matter (20-22%), and meteorological effects on particulates (14-16%) are the main factors influencing air pollution. PM_2.5was the dominant pollutant, impacting air quality on 84-88% of days, followed by ozone on 12-16%. Winter showed the best air quality, while summer had no "Good" days. Among the four areas studied, the desert suburb had the cleanest air, and the industrial area the most polluted. These findings offer crucial insights for air quality management in the region.

Assessment of air pollution dispersion during wet season: A case study of Rumaila Combined Cycle Power Plant, Basrah, Iraq

2024-10-14T00:42:47+00:00

This study presents an assessment of the levels of air pollution emanating from natural gas combustion at the Rumaila Combined Cycle Power Plant (CCPP) in Basrah City by using a Gaussian dispersion model, with results at distance of 100, 500 and 1000 m for the pollutants CO, SO₂, NO, and particulate matters (PM). Data on atmospheric stability assessments were taken from meteorological stations in Basrah Governorate that belong to the Iraqi Ministry of Agriculture. The study determined the emission rates from the five stacks of the plant and the wind speed, wind direction at stack height, and Turner-Pasquill stability classes for the wet conditions of 2023/2024. The following are the maximum pollutant concentrations emanating from the stacks: CO was 186 μg.m^-3 at 100 m, 6 μg.m^-3 at 500 m, and 2.5 μg.m^-3 at 1000 m; SO₂ was 0.5 μg.m^-3 at 100 m, 0.05 μg.m^-3 at 500 m, and 0.01 μg.m^-3 at 1000 m; NO was 0.07 μg.m^-3 at 500 m and 0.03 μg.m^-3 at 1000 m; PM was 11 μg.m^-3 at 100 m, 0.4 μg.m^-3 at 500 m, and 0.15 μg.m^-3 at 1000 m. All of these measurements are well below national ambient air quality standards, which means that the Rumaila power plant is not locally deleterious to the air quality.

Use of ERA5-L reanalysis datasets to derive heat units and predict the maturity period of wheat crop in central Punjab

2024-08-05T12:18:27+00:00

The ERA5-L reanalysis dataset, produced by ECMWF is the latest and most advanced global climate reanalysis datasets available with high spatial and temporal resolution. To assess the applicability of ERA5-L reanalysis data, a field experiment was conducted to predict the onset of maturity period of wheat crop based on heat units derived by ERA5-L data at the University research farm in Ludhiana. The wheat variety Unnat PBW-550 was sown under two dates of sowing (D₁: 27^th October and D₂: 17^th November) during three consecutive seasons (2020–21, 2021–22, and 2022–23). The phenological observations revealed that the October sown wheat took a greater number of days (153-154 days) to attain maturity as compared to November sown (139-142 days) crop. When heat units were derived from ERA5-L dataset, accumulated GDD (R²:0.95) and accumulated PTU (R²:0.95) displayed higher maturity prediction accuracy compared to HTU (R²:0.32) in all three rabi seasons. Ground observed and ERA5-L information were employed to estimate the beginning of maturity for wheat. For this, the accumulated heat units were calculated from sowing to booting stage of wheat crop. Our findings provided intriguing prospects for using ERA5-L reanalysis data as a different data source to predict crop phenology far in advance.

Trend analysis and change-point detection of temperature and rainfall in southern Peruvian Amazon and its relation to deforestation

2024-09-06T13:29:39+00:00

The study aimed to identify the change points, tendencies, and trends in climatic parameters (precipitation and temperatures) and to investigate their relationship with deforestation in the southeastern Peruvian Amazon (Tambopata). Rainfall and temperature data for the Puerto Maldonado station from 1970 to 2023 was used. Monthly, seasonal, and annual precipitation as well as temperature (maximum, minimum, and mean) were analyzed for possible trends using nonparametric Mann-Kendal statistic test, while the Pettitt test was employed to detect the abrupt change point in time series. The Spearman's correlation coefficient was used to identify the relationship between deforestation and climate parameters. The results revealed a rise in mean, minimum, and maximum temperatures. Mann Kendall and Sen’s slope revealed significant trends in the monthly, seasonal and annual temperatures in the study period. However, in contrast to the temperature variation trend, the monthly, seasonal and annual precipitation did not present a significant trend. Significant positive correlations were obtained between deforestation and temperatures but its association with precipitation was not significant.

Hydro-thermal conditions and their impact on irradiance and hygro-thermal use efficiency of wheat in central Punjab

2024-09-16T04:10:53+00:00

The experiments were conducted to develop indices to provide critical insights into crop phenology and yield responses of wheat to thermal and moisture conditions during rabi 2021-22 and 2022-23 at PAU, Ludhiana, Punjab. The wheat variety Unnat PBW was sown on three dates (D₁: 27^th October, D₂: 17^th November, D₃: 8^th December) under three irrigation regimes (I₁: irrigations at CRI, Jointing, 50% Flowering, Soft dough stages, I₂: irrigations at CRI, Flag leaf emergence, soft dough stage, I₃: irrigations at Jointing, Soft dough stage). Results revealed that the crop sown on 27^th October acquired maximum number of calendar days and various heat indices viz. growing degree days (GDD), solar thermal degree days (STDD) and hygrothermal units (HGTU) for attaining physiological maturity while 8^th December sown crop resulted in lowest accumulation. However, late sown crop was observed to accumulate more STDD. Wheat sowing before 17^th November resulted in higher grain yield and energy use efficiencies. Four irrigations at critical stages took highest GDD, STDD and HGTU for attaining maturity which was at par with two irrigations. Application of four irrigations resulted in significantly higher yield and energy use efficiencies. Similar performance of three irrigations was observed when irrigation was not skipped at anthesis stage.

Trend analysis of hydrometeorological parameters and reservoir level of Tarbela reservoir, Pakistan

2024-09-29T04:29:12+00:00

This study identified the trends in monthly temperature, precipitation, & evaporation for Astore, Darosh, Gilgit, Gupis and Skardu and trends in inflow, outflow and reservoir level for Tarbela reservoir. Two non-parametric tests i.e. Man-Kendall and Sen’s Slope and Innovative Trend Analysis (ITA) were used to determine the trend. The first Mann-Kendall test with a significance level 5% was applied to 33-year data of five selected areas from 1990-2022. The results showed an increasing trend in temperature in March for all selected areas. No significant trend was observed in precipitation except negative trends for Darosh in March, May and December and positive trend for Gilgit in January and September. It has been observed that the trend direction given by ITA and Mann-Kendall is similar. Inflows to reservoir were found directly related to the temperature because of glacier melt in rising temperature, thus increasing the inflow, although the precipitation was found decreasing with increase in temperature. Altered snowmelt patterns can influence weather systems, potentially contributing to more extreme weather events.

Trends of temperature and precipitation extreme indices in north Maharashtra

2024-09-26T01:12:59+00:00

Climate change has intensified extreme weather events, posing major challenges to agriculture-dependent regions like Northern Maharashtra. This study analyzed temperature and precipitation extremes across five districts—Nashik, Dhule, Nandurbar, Jalgaon and Ahmednagar using data from 1982 to 2022 with the help of RClimDex model. Key temperature indices, including tropical nights (TR25), warm days (TX90p), and frost days (FD13) showed an increase in warm events and a decline in cool nights and frost days. Reduced diurnal temperature range (DTR) indicated less nighttime cooling, consistent with global warming. For precipitation, extreme rainfall events are rising as indicated by the maximum 1-day precipitation (Rx1day), while consecutive dry days (CDD) are shortening. These shifts heighten risks such as crop heat stress, altered growing seasons, soil erosion, and water management challenges. The study underscores the urgent need for adaptive agricultural strategies, improved irrigation, and early warning systems to mitigate the impacts of climate change and enhance resilience in Northern Maharashtra.

Effect of changes in climatic variables on poultry egg production in Nigeria

2024-10-07T15:34:35+00:00

This study examined the effect of changes in climatic variables on poultry egg production in Nigeria using past data from 1963 to 2022. The study employed multiple regression analysis and descriptive statistics to meet its objectives. The study found a marginal increase in the trends of relative humidity, rainfall, temperature, and poultry egg production over the years. The regression analysis showed that the R² value indicated that 72% of the variation in poultry egg production could be explained by changes in relative humidity, rainfall, and temperature. Specifically, relative humidity (-2.91; p < 5%) and temperature (-27.9; p < 1%) had significant negative effects on egg production, while rainfall (1.31; p < 1%) had a significant positive effect. The study recommended that poultry farmers should adopt temperature management strategies, especially during periods of extreme heat, to reduce the adverse impact of high temperatures on egg production.

Comparative evaluation of different solar radiation models with Angstrom-Prescott model for Hazaribagh, Jharkhand

2024-09-26T01:11:54+00:00

This study evaluates the performance of six solar radiation models for Hazaribagh in Jharkhand by comparing their estimates with those derived from the Angstrom-Prescott (A-P) model, which served as the benchmark reference. The results revealed significant variability in model performance on both a monthly and seasonal basis. The Togrul-Onat and Ertekin-Xaldiz models tended to overestimate solar radiation, particularly during the summer months, while underestimating it in the remaining months. In contrast, the Ogelman model consistently underestimated solar radiation throughout the entire year. The Almorox-Hontoria model showed only minor overestimations in certain months, while the Chen model primarily overestimated during the spring and early summer. On a monthly scale, all selected models showed a positive correlation with the standard Angstrom-Prescott (A-P) model, with R² values ranging from 0.52 to 0.99. Notably, the Almorox-Hontoria model exhibited the strongest positive correlation (R² = 0.993) with the A-P model, identifying it as the most reliable for estimating solar radiation. On a seasonal scale, the models generally performed well, with R² values ranging from 0.85 to 0.99. However, the Togrul-Onat and Ertekin-Xaldiz models exhibited weaker correlations with the A-P model, particularly during the Zaid season, indicating their limitations in accurately estimating average daily solar radiation during this period. These results highlight the necessity of careful model selection and calibration to account for seasonal variability. Overall, the Almorox-Hontoria model demonstrated the highest accuracy and consistency across both monthly and seasonal scales, emphasizing the importance of adjusting models to specific temporal and geographic conditions.

Assessing and mapping the vulnerability index of Bangladesh to natural and climate-induced disasters: A spatial analysis at the subdistrict level

2024-09-12T16:07:55+00:00

This study assesses and maps the vulnerability index of Bangladesh at the subdistrict level to a range of natural and climate-induced disasters. Four vulnerability index maps are created using principal component analysis and categorized into five risk levels: (1) no/very low risk, (2) low risk, (3) moderate risk, (4) high risk and (5) very high risk for each sub-district. The results reveal that the south east region is highly vulnerable to cyclones, Haor region stands out as the most vulnerable area for flash floods, with numerous subdistricts facing very high to high risk levels; northern and north-eastern regions are prone to cold waves, while the western part of Bangladesh is highly vulnerable to heat waves. This comprehensive spatial analysis provides critical information for disaster risk reduction and adaptation strategies, assisting decision-makers in identifying the most vulnerable areas and prioritizing interventions. The findings of this study might be useful for policymakers as well as planners.

Meteorological and satellite-based data for drought prediction using data-driven model

2024-10-11T12:43:12+00:00

This work presents a data-driven model, the Artificial Neural Network-Multilayer Perceptron Neural Network (ANN-MLP), for use in meteorological drought deciles index (DDI) predictions over various climatic sub-zone. Two types of rainfall data from meteorological weather stations (WSs) and satellite-based estimates of PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network) were adopted. This work considered the calculated DDI (DDI original) from WSs to train and develop the proposed algorithm at three sub-zones (ANN-MLP-DDI models). The newly developed model was tested for DDI prediction using PERSIANN, and compared with the calculated DDI original from WSs. The results positively revealed that the ANN-MLP-DDI models showed high performance (Correlation coefficient r= 0.981) for DDI prediction against the DDI original from WSs. It can be concluded that data-driven models are feasible for drought prediction, and this work could help water managers in mitigating drought impacts and in providing information for policy makers

Agroclimatic characterization of Zamosc, Poland using hydrothermal coefficient (HTC)

2024-07-08T05:27:52+00:00

The hydrothermal coefficient (HTC), which is based on precipitation and temperature, is an important index used for characterizing the agroclimatic condition of a region. In the present study, 47 years (1976-2022) of data on air temperature and precipitation of Zamosc (Poland) were used to compute the hydrothermal coefficient (HTC) in different months of the growing season (April-October). Results revealed that temperature increased while precipitation decreased during the study period as a result the HTC also decreased. The average values of the HTC indicate deteriorating conditions for plant growth and development resulting from decreasing values of this coefficient. There was a clear increase in the number of months in which the value of the HTC≤1.3 coefficient corresponded to dry and extremely dry conditions. Particularly frequently dry and extremely dry conditions occurred in the second half of the growing season from July to October. Climate change is already causing a changing crop structure and the need to implement new technologies towards emerging threats. In order to ensure optimal plant yields, it is particularly important to rationally manage the available water resources.

Effect of temperature and moisture on soil pathogen Fusarium solani of lemon in Adjara, Georgia

2024-08-09T15:18:19+00:00

The research was conducted in the 2022 and 2023 seasons in the agrometerology and plant protection laboratory and citrus greenhouse of Batumi Shota Rustaveli State University, the aim of which was groupe and unit role of temperature and soil moisture (SM) content on aggressive soil pathogen F. solani on lemon, also effects of pathogen density and soil moisture on belowground and aboveground morphological traits. In our study, we could find that the both temperature and soil moisture played a decisive role in influencing the root rot disease scenario. As per the disease susceptibility index (DSI), a combination of high temperature (35°C) and low SM (60%) was found to elicit the highest disease susceptibility in lemon. High pathogen colonization was realized in lemon root tissue at all time-points irrespective of genotype, temperature, and SM. Interestingly, this was in contrast to the DSI where no visible symptoms were recorded in the roots or foliage during the initial time-points. For each time-point, the colonization was slightly higher at 35°C than 25°C, while the same did not vary significantly with respect to SM. Shoot biomass was not affected by either pathogen density or soil moisture. However, the two experimental factors have additive effects on the severity of leaf damage. Leaf damage increased with the density of F. solani in the soil, being significantly higher at 60 CFU/g and 120 CFU/g than in control seedlings. Leaf damage was higher at the two extreme soil moisture levels (15% and 100% WHC) than at the two intermediate levels (40% and 50%). In addition, differential expression studies revealed the involvement of defense-related genes, such as endochitinase and chitinase, in the resistant lemon cultivar Meyer, which contribute to retarding root rot disease progression in lemon. In the early stages of infection, especially with low SM. That can be beneficial for farmers and researchers who involve in Citrus

Weather based forewarning model for cotton pests using zero-inflated and hurdle regression models

2024-10-16T03:41:18+00:00

Early forewarning of crop pest based on weather variables provides lead time to manage impending pest attacks that minimize crop loss, decrease the cost of pesticides and enhance the crop yield. This paper is an attempt to forewarn incidence of Cotton pests using weather variables. The pest incidence data from 2015 to 2023 for Aphids, Jassids, Thrips, and Whiteflies has been used for the study. The pest incidence being count variable, different count regression models such as zero inflated Poisson & negative binomial, hurdle Poisson & negative binomial, negative binomial and generalized Poisson regression models have been developed for forewarning of pests. Results indicated that zero inflated Poisson regression model outperformed the other models with improved performance of nearly 30 to 75%. Thus, the zero inflated Poisson regression model is a reliable tool in prediction of cotton pests, thereby aiding towards better pest management strategies.