Journal of Agrometeorology

Rainfall variability and trends in different agroclimatic zones of Bihar, India

2024-09-03T04:26:31+00:00

Analysis of seasonal rainfall trends in Himachal Pradesh by Mann-Kendall and Sen’s Slope estimator test

2024-12-07T16:20:05+00:00

Analysis of SCATSAT-1 Gamma-0 and Sigma-0 products for agricultural land use applications

2024-12-25T09:56:56+00:00

Assessment of irrigation requirement and crop water demand of Bargarh canal command area: A CROPWAT-based Simulation Study

2024-11-28T04:32:51+00:00

Optimizing water usage for chilli (Capsicum frutescens L.) through drip irrigation using CROPWAT in Malang Regency Indonesia

2024-11-17T05:10:09+00:00

Climate-smart irrigation strategies for drip-irrigated exotic vegetables: An analysis of Bok choy, Chinese cabbage, Zucchini, and Broccoli in Jalandhar Punjab

2025-01-06T06:15:00+00:00

Variability and trends of rainfall in past and future in Kumaon region of Uttarakhand

2025-01-12T16:00:29+00:00

Effect of shades on growth, yield and quality of cherry tomato in Indonesia

2024-11-08T13:12:29+00:00

This study aimed to assess the effect of shade treatments on growth, yield, and fruit quality in two tomato varieties (cherry ruby and cherry sweet ruby). Treatments included the use of UV plastic shade, as well as its combination + 25% and 50% paranet. Parameters observed included light intensity, temperature, growth rate, plant height, number of leaves, leaf area, number of fruits, fruit weight, vitamin C content, and sweetness (Brix). Results showed that the addition of paranet significantly reduced light intensity and stabilised temperature, which had a positive impact on growth and vitamin C content but reduced sweetness. The cherry ruby variety showed superior performance in vegetative growth, fruit number and weight, and vitamin C content, while cherry sweet ruby excelled in sweetness. The combination of UV plastic shade + 25% paranet gave the best results in creating a balance between growth, yield, and quality. This study shows the importance of shade management in tomato cultivation in the tropics to optimise productivity and fruit quality.

Assessing the trend of weather parameters and their effect on rice and jute yields in southern part of West Bengal

2024-11-03T13:30:06+00:00

Considering the global concern on climate change, understanding the spatiotemporal variability of meteorological variables and their impact on crop yield is crucial for an agricultural nation like India. In this work, we have analyzed the temperature and rainfall data for seven decades (1951-2020) over the southern part of West Bengal represented by four grid points using Mann-Kendall test and Sen's slope estimator. The results showed no significant trend in average annual maximum temperature_, whereas, the average annual minimum temperature exhibited increase in trend at 95 % significance level among all the grids. However, the total yearly rainfall showed no trend, apart for the grid region centered in Bardhaman district showing an increasing trend. The correlation between 23 years (1997-2019) of yield data and climatic variables for different phenophases ranges from -0.47 to +0.72 for Kharif rice and -0.47 to +0.60 for jute. Climatic variables averaged over crop phenophases exhibit finer characteristics compared to annual averages and bear significant influence on yield variability as shown by multiple linear regression. Regression analysis indicate that temperatures play a more influential factor in determining Kharif rice yields than rainfall and yield equations pertaining to rice exhibits better sensitivity to varying climate than those representing jute yields.

Evaluation of NOAH-LSM model over Pune and Ranchi in different seasons

2024-12-13T13:47:38+00:00

Soil moisture (SM) and atmospheric parameters determine the surface energy partition, which impacts near-surface air temperature and moisture. Two locations, Pune and Ranchi, with different soil moisture (SM at Pune with clay soil is higher than that at Ranchi with loam soil), are chosen to evaluate the NOAH land surface model (NCEP, OSU-version 2.7.1) for winter, pre-monsoon and post-monsoon seasons. We have used the estimated surface fluxes by eddy covariance technique for the model validation. Agreement is better between the model and observations of net shortwave radiation for dry soil than that for wet soil, such a feature caused by surface albedo mismatch. Model validation of sensible (H) and latent (LE) heat fluxes at Pune indicate better agreement overall for winter (Jan; R² and RMSE for H, 0.72, 34) and post-monsoon (Nov; 0.67, 56) compared to summer, (May; 0.55, 70). Similar is the case at Ranchi, with R² and RMSE for winter and post-monsoon (January: 0.8, 24 & November: 0.9, 14) better and lower for summer (May: 0.7, 65). Bowen ratio (Model) for wet soil (0.45) is lower than that for dry soil (0.6). The model underestimates ground heat flux for wet soil and overestimates for dry soil due to soil thermal and hydraulic conductivity uncertainty. Further improvement of parameterization schemes in the land surface models would help better understand soil hydrology and boundary layer development.

Water footprint of wheat under different irrigation practices at Faridkot, Punjab

2025-01-07T07:38:41+00:00

Field experiments were conducted during Rabi seasons at Punjab Agricultural University, Regional Research Station, Faridkot, Punjab for 13 years (2010-11 to 2022-23) to assess the water footprint (WF) of wheat crop irrigated through different methods such as conventional surface flood (SF) during 2010-11 to 2018-19, surface drip (SD) during 2019-20 to 2020-21, and subsurface drip (SSD) during 2021-22 to 2022-23. Results elucidated that quantity of the irrigation water applied to the wheat crop ranged between 209 and 375 mm in different years. Whereas, wheat yield ranged from 3450 kg ha⁻¹ (2017-18) to 5471 kg ha⁻¹ (2021-22). Wheat crop under SF irrigation recorded higher WF_total 0.98 to 1.57 m³ kg⁻¹. The maximum rainfall 250.3 mm received in 2014-15 resulted highest WF_green (0.46 m³ kg⁻¹) and lowest WF_blue (0.45 m³ kg⁻¹). The wheat cultivation under SD and SSD reduced the WF_grey up to 35 % and WF_blue up to 35.0 – 42.8 % over SF. The higher crop yield and/or fewer water consumption both are associated with the lower WF. Therefore, for hydrological resource conservation and to ensure environmental sustainability, irrigation through SSD and SD should be promoted over the traditional SF method among the farming community.

Spatiotemporal Bayes model for estimating the number of hotspots as an indicator of forest and land fires in Kalimantan Island, Indonesia

2024-10-05T00:53:01+00:00

Forest and land fires often occur on the island of Kalimantan and have a widespread impact on neighboring countries. One indicator of forest and land fires is hotspot. Climate factors play an important role in determining hotspot patterns and trends in a location, which often fluctuate and are difficult to predict. This research aims to predict the number of hotspot spatially and temporally in the next month on Kalimantan Island and analyze the influence of local climate on hotspot events. The Bayesian Conditional Autoregressive method with Integrated Nested Laplace Approximation and optimal weight selection using Getis-Ord G are used to increase prediction accuracy. The distribution of hotspot is assumed to follow the Negative Binomial distribution. The research results show that the best model uses an additive approach and interaction with explanatory variables with a Deviance Information Criterion value of 97,799.8. Predictions from this model have a Root Mean Square Prediction Error of 7.08 and an Average Absolute Prediction Error of 0.63. However, the model still has limitations in predicting extreme events. Climatic factors such as low rainfall, long days without rain, high air temperatures, and low humidity contribute significantly to the increase in the number of hotspot in Kalimantan.

Green gram yield projections for Kibwezi east subcounty Kenya using the APSIM model under RCP's 4.5 and 8.5

2024-09-29T10:20:58+00:00

Green gram is widely grown in Kenya for food and income. However, climate change has shown unprecedented effects on its production in Kibwezi East Sub County affecting its yielding capacity. In this study, the Agricultural Production Systems Simulator Model (APSIM) (green gram module) was used to evaluate climate change impacts on its production by simulating the yields under present scenario (2001-2023), Representative Concentration Pathways 4.5 and 8.5 (2041-2071) climate scenarios. The model was parameterized and evaluated using soil data, daily climate data and phenological characteristics for three green gram varieties, Biashara, KS 20 and N26. Yield data was obtained from a field experiment carried out during the October - November – December (OND) planting seasons in 2020 and 2021.The developed models had a Coefficient of Determination (R²) ranging from 0.58 to 0.84 and a Root Mean Square Error (RMSE) ranging 3.0 and 13.3 meaning the models were reliable in simulating future yields. Model predictions showed that performance of green gram under RCP 4.5 and RCP 8.5 would greatly reduce. Varieties KS 20 and Biashara showed relatively high resilience to increased temperatures. This calls for employment of innovative and sustainable strategies for climate change adaptation.

Climate change impacts on the accumulation of growing degree days for corn in central Mexico

2024-10-28T11:44:32+00:00

Temperature is the main driving factor for plant development and growth and determines, in an important way, crop yields and is projected to increase under various climate change scenarios, which is expected to affect the thermo-sensitive crop like corn. Therefore, an attempt has been made to identify the spatio-temporal behavior of temperature, through the accumulation of growing degree days (GDD) in the Atlacomulco Rural Development District (ARDD) of Mexico. The maximum and minimum temperatures for the historical period 1985-2017 and projected under the SSP2-4.5 and SSP5-8.5 climate scenarios for distant time horizon (2061-2080) were analysed. It was identified that the average GDD in the ARDD corresponds to 1,440 ºC, being the northern zone the one with the highest accumulation with up to 1800 ºC. SSP2-4.5 identifies an increase of 298 ºC with respect to historical values, while SSP5-8.5 indicates the greatest increase of up to 33% in the accumulation of GDD with an average value of 1,914 ºC. The warming that is projected in the distant horizon allows identifying warm characteristics in the ARDD, which could increase corn production in this temperate climate.

Effect of sowing dates and nitrogen management on thermal time requirement and heat use efficiency of Rabi maize in Odisha

2024-09-26T01:27:09+00:00

Rapid climate changes are imperative and it is crucial to evaluate and find ways that work for nitrogen (N) fertilizer management with appropriate sowing dates to prevent critical growth stages from being impacted by changing climatic conditions. Considering this, a field experiment was carried out in the Rabi season of 2023-24 at the Post Graduate Research Farm of Centurion University of Technology and Management, Odisha. The experiment was laid out in a split-plot design with different sowing dates and nitrogen split applications at different times with varying proportions. The main plots consisted of four sowing dates and four nitrogen split applications were considered in sub-plots. Results revealed that the early sown crop (31^st October) took a higher number of days and heat units to attain various phenophases. Maize sown on 31^st October consumed maximum heat units of 1750 ℃ days and the significantly highest heat use efficiency (HUE) of 3.56 kg ha^-1℃ day^-1 for grain yield compared to minimum heat units (1665℃ days) and the significantly lowest HUE of 2.81 kg ha^-1℃ day^-1 for 15^th December sown crops. Among split applications of nitrogen, treatment consisting of 25% N basal+25% N prior to knee high + 25% N at knee high +25% N at tasseling recorded the highest HUE (3.55 kg ha^-1℃ day^-1). The sowing of maize crop on 31^st October with 25% N basal+25% N prior to knee high + 25% N at knee high +25% N at tasseling have been found to be the most efficient for heat utilisation.

Assessment of air pollution resulting from the South Baghdad power plant using the Gaussian model

2024-09-30T00:55:45+00:00

The city of Baghdad is currently facing a significant air pollution crisis due to increased industrial activity. Therefore, the assessment of concentrations of air pollutants specifically carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), nitrous oxide (N₂O), and methane (CH₄) at Al-Mustansiriya University, located approximately 10 km from the north of the South Baghdad Thermal Power Plant (SBTPP), has been made and the emission rates of these pollutants are estimated. The atmospheric stability was determined using a three-dimensional ultrasonic anemometer and stability classes were determined using the Monin-Obukhov method and applied Lagrange scale to calculate vertical and horizontal dispersion coefficients. We applied the Gaussian model to the dataset in July 2024, a month characterized by peak power generation and increased fuel combustion. The results showed that the vertical dispersion coefficient played an important role more than the transverse dispersion coefficient in measuring the dispersion of pollutants causing instability in the atmosphere. A significant peak around the tenth day of the month was observed, suggesting a change in winds, temperature, or weather patterns that influenced the dispersion and accumulation of these gases. The concentrations of the gases were found to vary with distance. The analysis indicated that the pollutants from the plant primarily dispersed in a north-westerly direction due to prevalent wind direction and their impact on areas near the Al-Mustansiriya University.

Effects of meteorological factors on greenhouse gas emissions at traffic intersections in Baghdad: a seasonal analysis

2024-12-03T07:24:21+00:00

Baghdad has experienced notable changes in its climate over recent decades. This study aims to evaluate the impact of meteorological factors, such as temperature, humidity, wind speed on greenhouse gas emissions at traffic intersections in Baghdad city. The study focuses, in particular, on comparing greenhouse gas concentrations during summer (June and July 2023) and winter (January 2024), highlighting how climatic conditions affect the accumulation and spread of these emissions. The results indicated that the greenhouse gas concentrations were much higher during summer than in winter because of high temperatures and strong winds contributing to the accumulation of gases in crowded areas. In contrast, high humidity and weak winds in winter contributed to the accumulation of gases differently. The analysis implies that low humidity during summer months because of heat and drought worsens greenhouse gas emissions, while small thermal changes affect the level of emissions from vehicle fuel. It explicitly shows the pivotal relationship that exists between climate conditions and emissions within a city, further pinpointing the human contribution— especially from traffic—to climate change, therefore meriting attention within urban planning in climate policies regarding emissions.

Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques

2024-12-08T09:34:50+00:00

For global food security, accurate large-scale wheat yield estimates are critical. The solar induced chlorophyll fluorescence is more sensitive to photosynthesis than any other vegetation indices, so it is crucial to uncover its potential for accurately predicting wheat yields. In the present study, we implemented three machine learning algorithms, support vector regression, Random Forest and XGBoost, one linear regression method, Least Absolute Shrinkage and Selection Operator regression, and one deep learning method, long short-term memory, to predict the wheat yield prediction from 2008 to 2019 using satellite data (SIF) and vegetation indices. The results indicated Support Vector Regression outperformed Long Short-Term Machine in wheat yield prediction. In comparison to coarse-resolution SIF products, the high-resolution SIF product offers superior prediction. The results emphasize that with high-quality SIF the crop predictions can be improved.

Relationships between daily solar irradiance and maximum temperature in Iraq

2024-08-31T04:21:37+00:00

This study investigates the relation between daily solar irradiance (SI) and maximum air temperature (Tmax) over six cities in Iraq (Basra, Shanafiya, Baghdad, Rutba, Kirkuk, and Shakhan) using NASA POWER data for the period 2014-2023. Results revealed that in the arid southern provinces such as Basra, the annual mean SI exceeds 5.6 kWh m^-2day^-1 and Tmax frequently exceeds 48°C during the summer months while in the northern provinces lower SI and Tmax values were observed. The seasonal variation indicated peak values of SI during June while peak values of Tmax were observed in July-August. A strong relationship between SI and Tmax were obtained with R² of 0.75 to 0.82 at different locations.

SARIMA-based time series analysis of rainfall and temperature for the Tarai region of Uttarakhand

2025-01-01T09:44:46+00:00

The study was conducted at the Department of Agrometeorology, GBPUAT, Pantnagar under the Gramin Krishi Mausam Seva (GKMS) Scheme. The meteorological data on temperature and rainfall for the period of 1990-2022 collected from the Norman E Borlaug Crop Research Centre, Pantnagar, were used to develop a seasonal model using MINITAB software. The software used the monthly input data for the years 1990-2017 to develop the best Seasonal Autoregressive Integrated Moving Average (SARIMA) model for forecasting the variables on monthly basis. The forecasted values using SARIMA for the time period (2018-2022) were used to validate the model against the observed data and it was observed that the SARIMA (0,0,0)(1,0,1)₁₂was found suitable for rainfall and minimum temperature prediction and SARIMA (0,0,2)(1,0,0)₁₂ for maximum temperature prediction.

Irrigation water requirement of drip irrigated tomato and capsicum under controlled and open-field environments

2024-10-03T15:30:52+00:00

Freshwater scarcity poses a major challenge for Indian agriculture. This study presents an optimized irrigation scheduling framework for tomato and capsicum cultivation under protected and open-field conditions in Jalandhar, Punjab, over the 2021 and 2022 seasons. The CROPWAT model was used to estimate reference evapotranspiration (ETo) and irrigation needs. Results showed that ETo and ETc values were consistently lower under protected cultivation due to microclimatic control, reducing irrigation requirements by up to 27% compared to open-field cultivation. Water use efficiency and yields improved significantly under protected cultivation, with increases of 96% and 43% for tomato, and 92.8% and 40% for capsicum. This study demonstrates that optimized irrigation scheduling and protected cultivation can conserve freshwater and enhance agricultural sustainability in water-limited regions like Punjab.

Relationship between vegetation cover and land surface temperature in Basra, Iraq

2024-11-26T16:03:12+00:00

This study aims to assess the changes in vegetation cover and its relationship with land surface temperature from 1990 to 2024 in Basra Governorate, southern Iraq. Satellite images from Landsat 5 and 8, in addition to remote sensing and GIS tools, were used to analyze the changes in vegetation cover and their effects on land surface temperature (LST). Non-supervisory classification based on normalized difference vegetation index (NDVI) threshold values was used to classify vegetation cover into four classes. The results showed a significant decrease in dense vegetation cover from 4.8% in 1990 to 1.0% in 2024. It was also noted that non-vegetation areas increased, rising from 67.4% in 1990 to 79.3% in 2024. In winter, dense vegetation decreased from 4.5% in 1990 to 0.8% in 2024, and non-vegetation areas increased from 67.5% to 79.7%. In addition, the coefficient of determination decreased from 0.11 in 1990 to 0.06 in 2024, indicating a decline in the effect of vegetation cover on the surface temperature of the earth due to rapid urban expansion, which contributes to climate change. The study emphasizes the need to develop strategies to preserve the environment and reduce the effects of desertification and climate change in Basra.

Prediction of pan evaporation in Chhattisgarh using machine learning techniques

2024-09-09T15:07:53+00:00

Accurate measurement or estimation of evaporation loss is crucial for developing and successfully implementing water resource management strategies, irrigation planning, reservoir management etc. To predict the pan evaporation (EP) accurately for Raipur, Jagdalpur, and Ambikapur stations of Chhattisgarh, four deep learning models and three machine learning models were used and a hybrid model using Deep Neural Network (DNN) and Random Forest (RF) was proposed. Simulation results demonstrated that the hybrid model (DNN+RF) outperforms the rest with R² of 0.964, 0.920, 0.894 for Raipur, Jagdalpur and Ambikapur respectively. It has been observed that the hybrid DNN+RF model demonstrated faster convergence compared to other models with high accuracy, making it efficient and well-suited for real-time applications such as irrigation scheduling and water resource management.

A comparative analysis of value-added forecasts of rainfall in different agroclimatic zones of Assam

2025-01-07T11:08:11+00:00

The present study is designed to investigate the skillfulness of value addition in the case of forecasted rainfall data in terms of the level of accuracy over the direct model-derived outputs with respective observed rainfall across six agroclimatic zones of Assam during the monsoon season in 2023. The district-wise daily data of three categories e.g., model forecast, value added and actual rainfall provided by India Meteorological Department (IMD) have been compiled agroclimatic zone wise and compared. Correlations and regressions were performed to examine the effectiveness of value addition. It was found that the value-added rainfall had higher correlations (r = 0.52 and R² = 0.26) with the actual rainfall compared to model forecast rainfall (r = 0.42 and R² = 0.20) in Assam. Hence, it can be said that the value-added data was more skillful in predicting rainfall compared to model forecast rainfall for the 2023 monsoon season.

Simulation of bio-physical parameters and yield of wheat under projected climate during mid-century

2024-12-24T05:36:14+00:00

The experiment was conducted at the research farm of Punjab Agricultural University, Ludhiana, during the rabi seasons of 2021-22 and 2022-23 to simulate biophysical parameters viz leaf area index (LAI), biomass, and yield for the mid-century (2040–2069) under SSP2-4.5 and SSP5-8.5 climate scenarios using validated CERES-wheat model. The duration of wheat was projected to be shortened while biomass accumulation was projected to increase. The LAI showed a noticeable decrement during the grain-filling stage. Furthermore, the model simulated decrease in yield by -5.78% and -3.32% for SSP2-4.5 and SSP5-8.5 scenarios, respectively. Among the phenology as simulated using CERES wheat model, the grain-filling stage was identified as the most sensitive period for biophysical parameters under both the projected climate scenarios.