Journal of Agrometeorology

Soybean yield prediction leveraging advanced ensemble machine learning models

2025-03-31T11:32:25+00:00

Effect of climatic parameters on tomato production in Nigeria

2025-04-01T16:54:42+00:00

Sugarcane yield forecasting using machine learning techniques in Udham Singh Nagar district of Uttarakhand

2025-01-23T15:58:20+00:00

Variability in hydrothermal coefficient (HTC) and productivity of pasture ecosystems of Tersko-Kuma lowland, Russia

2024-07-07T20:28:42+00:00

Thermal sensing of crop: An insight on water stress

2025-03-12T01:03:54+00:00

Investigation of changes in vegetation cover over Mosul and Wasit provinces of Iraq

2025-03-20T17:42:44+00:00

Millets as a dual-purpose crop for sustainable nutritional and energy security: A comprehensive review

2025-02-04T06:28:29+00:00

Millets are multipurpose crops that can grow in diverse climatic conditions and have high nutritional value. However, the nutritional importance and various climatic adaptability of millets are not well recognized. The current review emphasizes the response of millet to climate variability, the significance of value-added products, and the role of millet agro residues. This review paper is a summary of a total of 106 published articles from different database sources. The results revealed that millets are a high source of protein, fat, minerals, and dietary fiber and rich in micronutrients which are used to overcome malnutrition and non-communicable diseases. Foxtail millet uses 257 g of water to produce 1 g of dry biomass as compared to maize (470g) and wheat (510g) indicating its climate resilience. The study also indicates a potential possibility of utilization of millet biomass in the production of bioenergy which in turn promotes the sustainability of renewable energy. Hence, developing schemes such as distributing seeds, fertilizer, pesticides, subsidized credit facilities, and promoting various value-added products are the main options to promote millet for further cultivation and consumption.

The Agrometeorological edge: Transforming agriculture in a changing climate

2025-03-21T17:08:20+00:00

Agrometeorology can be summed up as being a critical node where meteorological science meets the agricultural sector. It is highly important in bringing adaptation tools and strategies to the farmers' realm in response to climate-related challenges. Agrometeorology discusses impacts from extreme weather events as well as the resource constraints in relation to the climatic changes through the center of early warning and climate-resilient agriculture. This paper focuses on the role of agrometeorology in reducing the adverse effects of climate change on farming through tools such as remote sensing, agroclimatic zoning, and advanced models. Case studies from India will demonstrate practical applications and transformative potential for agrometeorological services in developing sustainable, climate-smart agricultural systems. Several integration challenges, such as data gaps, infrastructure deficiencies, and lack of farmer awareness on integrating AI technologies that promise a revolution in precision farming, are high. This underlines the use of a multi-disciplinary approach, with appropriate robust policies and technologies, toward building agricultural resilience to a changing climate.

Diurnal variation of radiation components at three major phenological stages of Boro and Kharif rice under different management practices in West Bengal

2024-11-11T09:54:27+00:00

Present study quantifies and compares the diurnal variation of four components of PAR (APAR, IPAR, crop and soil albedo) and net radiation across three key phenological stages for Boro and Kharif rice under different management practices. Two consecutive field experiments were conducted during 2018 and 2019 at the D Block farm of Bidhan Chandra Krishi Viswavidyalaya, considering 18 treatment combinations of three rice varieties, three spacing and two seedling ages. Weekly observations were taken six times in a day with two hours interval. Results indicated that the maximum values of IPAR (93.53% and 82.62%) were recorded in Triguna variety and minimum value (90.02% and 78.62%) in Heera variety during the reproductive and vegetative stage at 11:30 AM and 5:30 PM respectively in Boro season but it reduced by 7-8% in the Kharif season indicating the influence of cloudy weather. Boro rice consumes the maximum amount of net radiation (689.32 W/m²⁾ in reproductive stage followed by 649.22 W m^-2 in vegetative and 549.22 W m^-2 in ripening stages for Triguna variety whereas Heera consumes lesser amount (489.23 to 600.22 W m^-2), which is significantly lesser in Kharif season. The study concluded that components of PAR and net radiation vary significantly across phenological stages, spacing and varieties while the ages of seedling (only 7 days difference) remain relatively unaffected.

Assessment of climate change impact on major crops of the southern agroclimatic zone of Tamil Nadu, India

2025-01-28T15:23:04+00:00

Climate change poses significant risks to crop production, endangering food security and farmer livelihoods. The southern agro-climatic zone of Tamil Nadu is particularly susceptible to droughts and floods. This study assessed the future impacts of climate change on crop yields using the DSSAT crop simulation model, with climate projections based on the EC-Earth statistical downscaled model under the SSP2-4.5 scenario for the baseline period (1985–2014) and near-century projections (2021–2050). Projections indicate a rise in annual mean maximum temperature of 0.4°C and a 7% increase in rainfall. Simulated yields of rice, maize, sorghum, and groundnut are expected to decline by 5.6%, 2.1%, 8.2%, and 7.6%, respectively, due primarily to heat stress during critical reproductive stages and altered rainfall distribution affecting crop water availability. In contrast, black gram yield is projected to increase by 4.8%, benefiting from enhanced CO₂ fertilization and improved rainfall during its growing season. The study highlights the significant effects of climate change on agricultural productivity and the urgent need for adaptation strategies, including drought-resistant crop varieties, modified planting calendars, and enhanced water management techniques to build regional agricultural resilience in Tamil Nadu.

Assessment of wheat yields under climate change based on RCA4 model simulations in Tiaret region, Algeria

2025-02-07T09:28:59+00:00

This study focuses on analyzing the effects of rainfall and temperature variability on wheat production in the Tiaret region of Algeria and evaluating the future climate change and its impact on winter wheat yields. We analyzed the temporal variability of rainfall, temperatures and wheat yield using long term data. The future climate change projection data for two projected periods (2021-2050 and 2071-2099), under two representative concentration pathways (RCP 4.5 and RCP 8.5) were obtained from the Africa-Cordex regional climate model. The Pettitt test highlighted a decrease of 30% in annual rainfall during 1950-2020 and an increase of 1.3°C in maximum temperature from 1980 to 2020. The Pearson coefficient correlation showed a significant positive correlation between yields and mean rainfall and a negative significant correlation with maximum temperature. Future average yields estimated by linear regression with rainfall and temperature showed that the yields will drop by 20% if no adaptation measures are undertaken.

Impact of climate variability on nutmeg production in the Banda Islands, Maluku, Indonesia

2024-12-22T14:39:14+00:00

Maluku, an archipelagic province with many small islands, is a key nutmeg-producing region in Indonesia. However, nutmeg production has declined over time for unclear reasons. This study aims to assess climate change and its impact on nutmeg production in the Banda Islands, Maluku, Indonesia, by analyzing long-term (1964–2023) rainfall trends and the relationship between climate variables and crop productivity. The relationship between climate variables and nutmeg production over a 15-year period was analyzed using Principal Component Analysis (PCA). The results showed annual rainfall in the Banda Islands increased by 13% in 1994–2023 compared to 1964–1993. The frequency of El Niño events decreased from 8 to 4, while La Niña events increased from 4 to 8 over the same periods. Nutmeg production is positively correlated with rainfall and relative humidity, while it is negatively associated with air temperature and sunshine duration.

Spatio-temporal variations in air pollutants and their impact on wheat crop production in eastern Uttar Pradesh

2024-12-29T00:56:50+00:00

Recently, air pollutants have posed significant hazards to agricultural production, emerging as a critical risk to global food security. To address this issue, the study examines the spatio-temporal variability of air pollutants, specifically aerosol optical depth (AOD) and black carbon (BC). It investigates the impact of these pollutants on wheat production in eastern Uttar Pradesh, India. The study uses MODIS satellite observations and MERRA-2 reanalysis data, the study analyzes monthly and seasonal variations in AOD and BC from 2001 to 2023. The findings highlight a noticeable rise in AOD and BC due to biomass/residue burning, fossil fuel emissions and transported dust. Backward trajectory analysis, conducted using HYSPLIT modeling, traces pollutant origins and transport pathways from regions like the Thar Desert and the Arabian Sea. Wheat yield assessments in districts such as Varanasi, Gorakhpur, and Sonbhadra reveal spatially variable impacts of aerosols on crop productivity. Elevated AOD levels are linked to reduced wheat yields in some districts, while BC exhibits minimal influence. This study underscores the critical need for targeted regional policies to mitigate air pollution and minimize its adverse effects on agriculture in this high-yielding wheat-growing region.

Empirically derived crop coefficient values for tomatoes grown in protected structures under climatic condition of Jalandhar, Punjab

2025-04-01T13:16:01+00:00

In modern agriculture, calculating the Crop Water Requirement (CWR) for tomato crops under protected cultivation often relies on FAO-56 crop coefficient (Kc) values. However, these values may not fully account for the unique microclimatic variations within protected structures, creating a need for adjusted Kc values. This study aimed to develop growth-stage-specific Kc values for tomatoes grown under protected conditions in Jalandhar, Punjab. Results showed that daily microclimatic parameters, excluding relative humidity, were consistently higher in open-field conditions and lowest within protected environments. Pooled data indicated growth-stage-specific Kc values of 0.51, 1.05, and 0.61 for shed net houses; 0.53, 1.08, and 0.63 for polyhouses with insect net ventilation; and 0.51, 1.10, and 0.67 for open-field conditions, corresponding to the initial, mid, and late growth stages, respectively. Water consumption was highest during the mid-stage, decreasing progressively toward crop maturity. These empirically derived Kc values support precise CWR calculations through climatological irrigation scheduling, benefiting tomato cultivation in protected environments and similar agro-climatic regions. The development of growth-stage-specific Kc values provides a scientific foundation for improving irrigation water management and resource efficiency, offering valuable insights for farmers, policymakers, and water resource planners.

Combined effect of rainfall and sunshine duration on cassava output in Nigeria

2025-03-24T00:15:01+00:00

This study examined the combined effect of rainfall and sunshine duration on Casava production in Nigeria from 1988 to 2023 using secondary data obtained from the Nigeria Meteorological Agency (NiMet) and the Food and Agriculture Organization (FAO). Econometric techniques were employed to analyse the relationships between the variables. Regression analysis revealed that cassava yield was significantly negatively related with rainfall (β = -73,133.9; p = 0.000), while sunshine duration was positive and significant (β = 8,755,640.3; p = 0.000). Rainfall and sunshine duration interaction was significant and positive (β = 7,611.8; p = 0.000) and revealed that sunshine duration was mediating the adverse effect of excessive rainfall. The model explained a high explanatory power with R-squared being 0.896 and adjusted R-squared being 0.881. The error correction model (ECM) coefficient (-0.934) showed a high speed of adjustment towards equilibrium, which meant that the deviations in the output of cassava as a result of climate variability were adjusted quickly. Based on the findings of this study, farmers are encouraged to adopt water management practices and improved agronomic practices in an attempt to enhance the positive effects of sunshine duration on cassava yield.

Rainfall and temperature trends in northeastern states of India: Implications for agricultural productivity

2025-02-07T00:58:24+00:00

We analyzed the changes in annual and seasonal rainfall and temperature across seven northeastern Indian states for the period 1970-2023. Our findings indicate significant changes in climatic conditions, including both maximum and minimum temperatures and rainfall. Specifically, there has been a significant decline in seasonal monsoon rainfall, with overall annual rainfall also decreasing, except in Mizoram. Over the past five decades, there has been a marked increase in both maximum and minimum temperatures, though the extent of this warming differs by state and period. Temperature trends show a clear rise in both maximum and minimum temperatures. Considering the agricultural dependence in these states, we also examined the impact of climatic factors on Assam's agricultural output using Autoregressive Distributed Lag (ARDL) model. Findings show that rainfall significantly affects food grain production in Assam, whereas temperature does not significantly impact food grain production.

Repercussions of climatic variabilities on tea production in Nilgiris district of Tamil Nadu, India

2025-02-07T09:28:30+00:00

The Nilgiris district of Tamil Nadu is one of the world’s high-quality tea-producing region. This study focused on the changing climatic condition on tea production in Nilgiris, Tamil Nadu, India, utilizing the data from 1991 to 2020 employing the ARDL-error correction model, Findings revealed that tea production is stable for past decades, positively influenced by minimum temperature, and relative humidity while negatively influenced by precipitation. Thus, understanding the intricate relationship between climate factors and tea production is crucial for sustainable agricultural practices in the region, with implications for adaptation strategies amidst changing climatic patterns.

Agroclimatic suitability analysis for oil palm under projected climate in North Aceh Regency, Indonesia

2024-08-09T07:06:00+00:00

Climate change has led to changes in agroclimatic suitability classes for oil palm cultivation due to rising temperatures and changing rainfall patterns. North Aceh, one of the largest oil palm-producing regencies in Aceh Province, Indonesia is vulnerable to global climate change. The objective of this study is to identify suitable locations within North Aceh Regency for the cultivation of oil palm plants using average monthly rainfall and air temperature for the period 2014-2023 and also for future by employing projected climate data from the SSP2-4.5 scenario for the periods of 2026-2035 and 2036-2045. This study aims to identify changes in agroclimatic suitability classes for oil palm in North Aceh due to climate change. The research results show a significant shift from the highly suitable class (S1) to the moderately suitable class (S2) in the projection period of 2026-2045. These findings indicate a potential decrease in oil palm productivity, which could significantly impact farmers' incomes and the local economy. Therefore, adaptation policies that support the use of climate-tolerant varieties and the implementation of sustainable land management practices are needed to mitigate the impacts of these changes.

Estimation of minimum assured rainfall using probability of exceedance: A suitable approach for planning rainfed rice

2025-04-27T14:34:18+00:00

Rainfall is one of the most important components of agricultural productivity as it forms the basis of rural livelihoods and food security. In rainfed agricultural regions, where irrigation infrastructure is limited, the rainfall variability directly influences planting schedules, crop growth stages, and yield outcomes. The study presents a systematic approach for estimating minimum assured rainfall using the probability of exceedance (P) by analyzing 50 years (1973–2022) of weekly rainfall data from three Agro-climatic zones of West Bengal: Undulating Red and Laterite Zone, Gangetic Alluvial Zone, and Terai-Teesta Alluvial Zone. Using the CumFreq software, various probability distributions were fitted to historical rainfall data corresponding to the Standard Meteorological Weeks (SMW) 20 to 41. The best-fit models, primarily Generalized Laplace, Generalized Extreme Value (GEV), and Generalized Normal were used to estimate weekly rainfall at 25%, 50%, 75%, and 90% exceedance probabilities. The 75% probability level, representing assured rainfall, was used as a threshold to determine the number of rainy days and guide varietal recommendations for rice cultivation. Results revealed significant variability in rainfall patterns across zones, with notable implications for selecting suitable rice varieties. The findings provide a probabilistic framework to inform agricultural planning and risk mitigation strategies, especially under increasing climate variability in rainfed ecosystems.

Probability of occurrence of high temperature events during reproductive phase of wheat in Punjab

2025-01-06T06:14:24+00:00

A study was conducted with an objective to analyze how often high temperature events occur during the reproductive phases of wheat (January-March) in Punjab. Historical temperature data was analyzed to understand the probability of occurrence of temperature higher than the mean and its different combinations (mean+0.5SD, mean+1.0SD, mean+1.5SD and mean+2SD) during different standard meteorological weeks (SMW). It was found that at Ludhiana (central Punjab) the highest probability of maximum temperature (Tmax) and minimum temperature (Tmin) being higher than range was 16.9% and 18.8% during 9^th and 4^th SMW, respectively. At Ballowal Saunkhri (northeastern Punjab) the maximum probability of occurrence of Tmax higher than range was 16.4% during 6^th and 12^th SMW, respectively and that for Tmin higher than range was 20% during 9^thSMW. At Bathinda (southwestern Punjab) the highest probability of occurrence of Tmax and Tmin above range was 19.7% and 19.4% during 13^th and 11^th SMW, respectively. In northeastern and southwestern regions of Punjab the probability of having Tmax and Tmin above range was maximum during 12-13^th and 9^th-11^th SMW, respectively, while in central region it was maximum during 9^th and 4^th SMW, respectively. This implies that wheat crop should be managed adequately during these periods to avoid damage due to heat stress.

Spatiotemporal air quality variation between urban and agricultural areas: the influence of climatic factors and pollution dynamics

2025-03-27T22:26:07+00:00

Air pollution is a critical environmental and meteorological concern, significantly influenced by climatic conditions and atmospheric dynamics. Hence, the present study examines seasonal and daily variations in the concentrations of air pollutants, such as CO, SO₂, NOₓ, and O₃, in Baghdad, based on the principle of meteorological influences during the wet and dry seasons. Data were collected at 23 transportation intersections in addition to an agricultural area during morning and afternoon periods. The results indicated a robust relationship between the levels of pollutants and meteorological conditions. Carbon monoxide showed an afternoon peak during the dry season owing to air stagnation (28.2–18.7 ppm). Ozone concentrations also heightened in this season due to increased temperature and photochemical reactions (0.515–0.35 ppm). Levels of ozone, sulfur dioxide and nitrogen oxides reflect over-national air quality standards, with maxima seen in the dry season and more so in the afternoons. The results of the Air Quality Index analysis show that the main factors causing a drastic decrease in quality during the dry season are higher temperatures, scanty rainfall, and increased levels of photochemical activity. This result emphasizes the need to integrate meteorology into urban planning to mitigate pollution.

GIS-based suitability analysis of agrometeorological stations in Pampanga, Philippines

2025-01-24T03:18:07+00:00

This study aimed to identify suitable sites for agrometeorological (agromet) stations in Pampanga, Philippines using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). The study evaluated multiple criteria such as slope, land use/land cover, accessibility, proximity to water bodies, existing weather stations, and host institutions, all are based on World Meteorological Organization (WMO) guidelines. For factor weighting, the experts performed AHP and GIS tools facilitated spatial analysis, including rasterization, reclassification, and buffer zoning. Results indicate that Floridablanca, Minalin, Candaba, and Arayat are highly suitable for agromet station establishment, while areas like Angeles City and Mabalacat exhibit localized constraints. This study highlights the importance of a comprehensive and strategic approach to weather station placement, highlighting Pampanga's potential for enhanced weather monitoring and agricultural support. The findings showed a framework for sustainable agromet infrastructure development, addressing both academic and community needs.

Estimation of climatological parameters using ANN and WEKA models in Diyala Governorate, Iraq

2025-02-10T09:10:45+00:00

Artificial Neural Networks (ANN) and Waikato Environment for Knowledge Analysis (WEKA) model were used to estimate the climatic parameters viz. minimum temperature (T_min), maximum temperature (T_max), relative humidity (RH), wind velocity (WV) using the time series of monthly data for the period of 1980 to 2022. It was found that the estimation of the climate parameters using the two methods (WEKA and ANN) obtained acceptable values of correlation (R²) and error standards (RMSE and MAE) between the observed and estimated values, but they differed in accuracy. The WEKA method obtained better values in the estimation of the T_min component than ANN while the estimation of the T_max, RH, WV, the ANN method was better than the WEAK model in the estimation of the parameters.

Applicability of machine learning models for drought prediction using SPI in Kalahandi, Odisha

2025-02-07T09:23:24+00:00

This study assesses the performance of auto-regressive integrated moving average (ARIMA), artificial neural network (ANN), support vector machine (SVM) and extreme learning machine (ELM), in predicting meteorological drought with Standardized Precipitation Index (SPI-6 and SPI-12) for Kalahandi district, Odisha. Mann-Kendall tests showed no significant trend in SPI value for both shorter and longer scales. Model performance was evaluated using correlation coefficient (CC), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE) during the training as well as testing phases. For SPI-6, ARIMA performed well during training (NSE = 0.66, RMSE = 0.60) but showed a decline in testing (NSE = 0.25). Machine learning models, including ELM, SVM and ANN exhibited better consistency, with NSE values ranging from 0.45 to 0.47. For SPI-12, ANN delivered the highest accuracy with NSE values of 0.91 and 0.89 and RMSE values of 0.31 and 0.29 in training and testing, respectively. Graphical analysis further demonstrated that ANN and SVM outperformed ARIMA by effectively capturing nonlinear trends and extreme fluctuations. Overall, machine learning models, particularly ANN and SVM, proved to be superior for predicting both long-term (SPI-12) and short-term (SPI-6) precipitation indices, highlighting their effectiveness for accurate drought forecasting.

Innovative trend analysis of rainfall and temperature over Nubra Valley, Ladakh

2025-02-28T15:54:34+00:00

Climate change impacts on mountains have increased over the past few decades, with evident and significant consequences for people and ecosystems. This study investigates the long-term trend in rainfall and temperature (1951-2022) over Nubra Valley, Ladakh, India employing the Innovative Trend Analysis (ITA) using IMD gridded (0.25 x 0.25) rainfall and (1.0 x 1.0) temperature data. The results of ITA indicate no trend in May for 70% of areas, in November in south-western part, and in December in northern parts of the valley. A notable trend only in November in selected areas of central and northern part of the valley. Most of the months showed a rising pattern in rainfall. The tmin trend reveals an increasing trend in months, except for no trend in January and October in the southern region; there is a significant decreasing trend in February and July in the southern region. The tmax trend even shows an increasing trend, excluding June, displaying a significant decreasing trend. January and October in the eastern region and November in the western show no trend. The results are indicative of a rising rainfall and temperature trend in the Nubra Valley.