Journal of Agrometeorology

A bibliometric analysis of the Journal of Agrometeorology (JAM) from 2008 to 2022

V. KALAIMATHI — 2024-03-01

A quantitative analysis of scientific articles published in the Journal of Agrometeorology (JAM) between 2008 and 2022 was conducted using a variety of scientometric indicators. Various metrics were utilized to examine aspects including yearly research output, highly referenced sources, author rankings, contributions and profiles, cooperation trends, highly contributing nations, most cited papers, commonly searched keywords and worldwide collaboration mapping. This study employs biblioshiny for analysis and only looks at data that is available in Scopus database. With an h-index (17), a g-index (21) and 3238 total citations across the study period, the journal demonstrated considerable influence. With the greatest number of research publications (n=46) and the greatest number of citations (236), Pandey V stands out among other authors. In terms of the number of papers and citations, India emerged as the leading nation, with the Punjab Agricultural University in the lead with 744 publications. Four clusters were found by co-citation network analysis, with Allen RG being the most quoted author among them. The study also highlighted the fact that Indian authors worked together the most. This analysis is important for assessing the influence of the JAM and offers insightful information about noteworthy research trends and developments in the scientific community.

Surface energy fluxes and energy balance closure using large aperture scintillometer-based ET station on heterogeneous agricultural landscape in north India

ABHISHEK DANODIA — 2024-03-01

This study was carried out to understand the pattern of surface energy fluxes over a periodical scale and energy balance closure using Large Aperture Scintillometer and Micrometeorological tower. The standalone technique as ‘Scintillometry’ which observes the structure parameter of refractive index based on Monin-Obukhov Similarity theory, has the potential to measure the sensible heat flux precisely. This paper discusses the surface energy balance components and energy balance closure over a period of August 2017 to June 2018. The maximum mean energy fluxes Rn, G, H and LE were observed in September (98.6 Wm^-2), May (13.9 Wm^-2), June (53.3 Wm^-2) and August (82.1 Wm^-2), respectively. The overall mean ET was observed at the rate of 1.36 mm day^-1 during the study period. This scintillometry technique may further use in evapotranspiration modelling from polar orbiting satellite to geostationary satellite over a heterogeneous and undulated landscape.

Microclimatic study under wheat, mustard and chickpea crops in western plain zone of Uttar Pradesh

ABHISHEK PAINKRA — 2024-03-01

The present study to quantify the variations in microclimate under wheat, mustard and chickpea crops was conducted at ICAR-Indian Institute of Farming Systems Research, Modipuram, Meerut (U.P.), India. Air temperature, relative humidity, CO₂ concentration below and above the canopies of wheat, mustard and chickpea were recorded at hourly interval from 07:30 to 17:30 hours at different heights (0.5 m, 1.0 m and 2.0 m) from the ground. CO₂ probes like GMP-343, (Diffusion aspiration) was used to record data of CO₂concentration and HPM-75 probes was used to capture the observation of air temperature and relative humidity. Results revealed that diurnal air temperature was continuously increasing from morning to afternoon hrs and highest air temperature was recorded at 13:30 hrs (afternoon). Thereafter, air temperature decreased and reached to the lowest at 17:30 hrs irrespective of crops and height from the ground. Analysis of diurnal air temperature variations at different height clearly showed that tall statured crop such as wheat and mustard reached higher air temperature regime early (13.30 hrs) compared to short statured crop like chickpea at 15.30 hrs. Highest relative humidity was observed at 07.30 hrs and lowest relative humidity was recorded either at 13:30 hrs or at 15:30 hrs. As per study maximum CO₂ concentration was found at 07:30 hrs morning and minimum at 15:30 hrs. The lowest concentration of CO₂ (624 ppm) was recorded from the chickpea field and highest from the mustard field (630 ppm) at the same point of observation during vegetative phase of crops.

Potential yield of world maize under global warming based on ARIMA-TR model

CHENGZHI CAI — 2024-03-01

With continuous increase of population and demand for nutritional food, analyzing potential yield of world maize affected by global warming is of great significance to direct the crop production in the future. Thus, in this paper both average and top (national) yields of world maize between 2021 and 2030 are projected creatively using ARIMA-TR (Auto-regressive Integrated Moving Average and Trend Regression) model based on historic yields since 1961. The impact of global warming on the yields of world maize from 1961 to 2020 was analyzed using unary regression model. Our study concludes that between 2021 and 2030, average yield of world maize is projected to be from 5989 kg ha^-1 to 6703 kg ha^-1 while the top yield from 36530 kg ha^-1 to 44271 kg ha^-1, or the average ranging from 16.39% decreasingly to 15.14% of the top; from 1961 to 2020 global warming exerts positive effect on average yield of world maize less than on the top, which partly drives the gap between these two yields widened gradually; for world maize by 2030, the opportunities for improving global production should be mainly dependent on the advantage of high-yield countries.

Multistage sugarcane yield prediction using machine learning algorithms

SHANKARAPPA SRIDHARA — 2024-03-01

Sugarcane is one of the leading commercial crops grown in India. The prevailing weather during the various crop-growth stages significantly impacts sugarcane productivity and the quality of its juice. The objective of this study was to predict the yield of sugarcane during different growth periods using machine learning techniques viz., random forest (RF), support vector machine (SVM), stepwise multiple linear regression (SMLR) and artificial neural networks (ANN). The performance of different yield forecasting models was assessed based on the coefficient of determination (R²), root mean square error (RMSE), normalized root mean square error (nRMSE) and model efficiency (EF). Among the models, ANN model was able to predict the yield at different growth stages with higher R² and lower nRMSE during both calibration and validation. The performance of models across the forecasts was ranked based on the model efficiency as ANN > RF > SVM > SMLR. This study demonstrated that the ANN model can be used for reliable yield forecasting of sugarcane at different growth stages.

Developing weather-based biomass prediction equation to assess the field pea yield under future climatic scenario

AISHI MUKHERJEE — 2024-03-01

The present research focuses on the variation of field pea production under different prevailing weather parameters, aiming to develop a reliable forecasting model. For that a field experiment was conducted in New Alluvial Zone of West Bengal during 2018-19 and 2019-20 with three different varieties (VL42, Indrira Matar, Rachana) of this region. Biomass predicting equation based on maximum temperature, minimum temperature and solar radiation was developed to estimate field pea yield for 2040-2099 period under SSP 2-4.5 and SSP 5-8.5 scenarios. It reveals that solar radiation positively influences crop biomass, while high maximum and minimum temperatures have adverse effects on yield. The developed forecasting equation demonstrated its accuracy (nRMSE=17.37%) by aligning closely with historical data, showcasing its potential for reliable predictions. Furthermore, the study delves into future climate scenarios, showing that increasing temperatures are likely to impact field pea yield negatively. Both biomass and yield showed decreasing trend for the years from 2040 to 2099. SSP 5-8.5 scenario, which is more pessimistic one, foresees a substantial reduction in crop productivity. This weather parameter-based biomass prediction equation can be effectively utilized as a method to assess the impact of climate change on agriculture.

Keywords: Field pea, weather parameters, crop yield prediction, New Alluvial Zone, nRMSE

Analyzing the efficiency of Arduino UNO microcontroller in monitoring and controlling the microclimatic parameters of greenhouse

F. A. JOLLY — 2024-03-01

At present greenhouse farming has become more popular in contrast to traditional farming because of its adjustment capability of the environmental parameters such as temperature, humidity, light intensity, and soil moisture according to the requirements of the crops. Continuous monitoring and controlling facilities of the greenhouse system allow the farmers a good maintenance system with good quality and high yield of the crops. In this paper, an Arduino microcontroller was used in a greenhouse system for an automatic monitoring system for cultivation incorporating various sensors such as a temperature-humidity sensor, and soil moisture sensor to collect parameters for monitoring the environment of the greenhouse. The collected data were used to control the temperature using cooling fans which facilitated the greenhouse controlling the environment. For storage and processing the data the controller code was generated in the Arduino programming language, and finally inserted into the Arduino UNO R3 microcontroller. A solar power system with a rechargeable battery was installed as a source of energy to ensure continuous power supply to the greenhouse system. Implementation of a greenhouse with a microclimatic parameter monitoring and controlling system will result in mitigating land and labor requirement problems for small-scale farmers, and gardeners as well as supplying suitable data for agricultural researchers.

Evaluation of soft-computing techniques for pan evaporation estimation

AMIT KUMAR — 2024-03-01

Estimation of pan evaporation (E_pan) can be useful in judicious irrigation scheduling for enhancing agricultural water productivity. The aim of present study was to assess the efficacy of state-of-the-art LSTM and ANN for daily E_panestimation using meteorological data. Besides this, the effect of static time-series (Julian date) as additional input variable was investigated on performance of soft-computing techniques. For this purpose,the models were trained, tested and validated with eight meteorological variables of 37 years by using preceding 1-, 3- and 5- days’ information. Data were partitioned into three groups as training (60%), testing (20%), and validation (20%) components. It was observed that the models performed well (best) with preceding 5-days meteorological information followed by 3-days and 1-day. However, all LSTMs simulated peak value of E_pan was more accurate as compared to lower values. Meteorological data with julian date improved the performance of LSTMs (0.75<NSE 1; PBias< 10; KGE 0.75). The ANN trained using only meteorological data (preceding 5-days information) had better performance error statistics among all other ANNs and LSTMs with minimum MAE (0.68 to 0.86), RMSE (0.93 to 1.22), PBias (-0.73 to 2.44) and maximum NSE (0.83 to 0.84) and KGE (0.89 to 0.92). Overall, it was inferred that the forecasting of meteorological parameters using a few days preceding information along with Julian date as the time series variables resulted in better estimation of E_pan for the study region.

Machine learning methods for estimating reference evapotranspiration

AMIT BIJLWAN — 2024-03-01

Precise estimation of evapotranspiration is crucial for optimizing crop water uses particularly in the context of agriculture and horticultural production. In this study, various machine learning techniques was used to determine reference evapotranspiration by leveraging historical weather data. The models tested include artificial neural networks (ANN), Lasso, Ridge, Random Forest, LGBM regressor, and Gradient boosting regressor. LGBM regressor emerged as the top-performing model, exhibiting exceptional accuracy with a testing R-squared of 1.0. ANN also demonstrated notable performance, achieving a testing R-squared of 0.99. Moreover, the Random Forest and Gradient boosting regressor models showcased strong predictive capabilities, with R² values of 0.99 and 0.98, respectively. These models offer valuable alternatives for estimating evapotranspiration, providing robustness and adaptability to diverse environmental datasets.

Effects of pan evaporation-based drip irrigation levels on performance of guava grown in Udaipur and Rewa regions of India

S. S. LAKHAWAT — 2024-03-01

A field experiment was conducted for three years (2019-20, 2020-21 and 2021-22) on 4 years old guava orchard established at 3×2 m spacing with drip irrigation treatments at two locations viz. Udaipur Rajasthan and, Rewa, Madhya Pradesh. Plant growth, yield contributing parameters, fruit yield and water use efficiency was significantly affected by different pan evaporation-based drip irrigation levels (70, 80, 90 & 100% of Epan) over local control. In existing climatic conditions of Udaipur and Rewa regions, the daily irrigation water requirement of high-density planting guava tree was varied from 7.8 to 26.3 and 4.5 to 26.5 liter/plant/day, respectively. Among all the pan evaporation-based drip irrigation levels, the irrigation supplied at 80% and 90% of daily pan evaporation were found as best approach for irrigating high density plantation (HDP) guava orchard through drip irrigation in Udaipur & Rewa regions with maximum fruit yield (37.3 & 30.7tha^-1), irrigation water use efficiency (0.359 & 0.263tha^-1-cm) along with significant water saving (29.2 & 22.2%), respectively over local control. Results will help farmers, policy makers and irrigation managers to conserve available fresh water resources in water scares regions of Rajasthan and Madhya Pradesh.

Variation of standardized precipitation index (SPI) over southern West Bengal and its effect on jute yield

KAUSHIK MAITY — 2024-03-01

West Bengal is a key producer of raw jute fiber in the country. Identifying and managing dry spells during the jute growing period is crucial, necessitating contingency crop planning for enhanced productivity. Keeping this view in mind, standardized precipitation index (SPI) was calculated over five locations, representing five different districts of southern West Bengal. These locations are Barrackpore (North 24 Parganas District), Panagarh (Burdwan District), Bagati (Hooghly District), Krishnanagar (Nadia District) and Uluberia (Howrah District). This rainfall dependent dryness index (SPI) was calculated in 1 month and 3 months interval to identify short term dryness as well as mid-term dryness, applicable for seasonal crops. The trend analysis of the SPI values indicated that North 24 Parganas and Nadia experienced increased dryness during vegetative phase of Jute. Nadia district showed a significant increase in both short term and long-term dryness. The yield reduction index is well correlated with SPI values in all the study locations except Howrah. Arrangement of irrigation during the early stages of Jute can help the crop to cope up with the break of monsoon in this region

Assessing the influence of elevation on satellite derived normalized difference vegetation index and land surface temperature in Rajasthan

LAL CHAND MALAV — 2024-03-01

Land surface temperature (LST) and its interaction with normalized difference vegetation index (NDVI) is crucial for better understanding of environmental changes in current scenario. However, very few or scanty research on the interrelationship between LST, NDVI and topographic elements has been done in India. Therefore, the purpose of conducting this study was to examine, how LST and NDVI change as a function of elevation in Rajasthan. In present study, MODIS derived NDVI and LST and digital elevation model (DEM) from shuttle radar topography mission (SRTM) have been used. Results revealed that the LST and NDVI both were significantly influenced by elevation. Elevation, NDVI and LST varied from -6 to 1698 m, -0.09 to 0.65 and 24 to 45°C throughout the study region. In contrast to LST, which has a decreasing gradient from western to eastern portions, the spatial variability of NDVI has decreasing gradients from southern and eastern to western regions. The highest mean LST value (39.76 ± 0.2.9 ⁰C) was obtained at an elevation range of -6 to 168 m, whereas NDVI value (0.38 ± 0.06) at elevation ranges of 589 – 1698 m. The analysis of the correlations between LST, NDVI and elevation indicated that the elevation has strong positive correlation with NDVI (r² = 0.26) and negative correlation with LST (r² = 0.28). Findings from this kind of research can be utilized as a platform for environmental and land use planning for sustainable ecosystem management.

Climate changes impact on the distribution of vegetation in Wasit and Nineveh regions of Iraq

DHER I. BAKR — 2024-03-01

Climate changes have a direct or indirect impact on many vital systems, including human and animal, as well as vegetation. The monthly precipitation and temperature for the period (1981-2021) and vegetation images (NDVI) for the period (2000-2022) from the satellite (NASA) for the regions of Ninevah and Wasit of Iraq were used to find out their variations over the space and time. It was found that the temperature was increasing with time, but the precipitation was in a state of turbulent increase in the two study areas. The distribution of vegetation was also in a state of change with time as well as within a region. The vegetation area increased with increase in precipitation which was greater in the Ninevah region than in the Wasit region. When there was a lack of precipitation, the vegetation cover area decreased in the two study areas. The increase in temperature also resulted in a decrease in the density and area of vegetation. It was found that the change in the amount of precipitation was more influential than the change in temperature on the vegetative distribution.

Rice brown planthopper, Nilaparvata lugens (Stål) feeding behavior in relation to elevated CO2 and temperature

V. SUNIL — 2024-03-01

Feeding behavior of brown planthopper, Nilaparvata lugens (Stål) populations collected from different geographical regions Ludhiana, Nalgonda and West Godavari at three generations 1, 5 and 10 fed on rice plants grown under different CO₂ and temperature levels 1) Ambient CO₂@ 380±25ppm + ambient temperature (aCO₂+aT), 2) Elevated CO₂@500±25ppm + ambient temperature (eCO₂+aT) and 3) Elevated CO₂@500±25ppm + elevated temperature (eCO₂+eT) in closed CO₂ chambers was studied. Elevated CO₂ + elevated temperature increased feeding rate of BPH nymphs while BPH adults showed declined feeding rate. BPH nymphal feeding rate increased with progressive generations while it decreased in BPH adults. Ludhiana adult BPH population showed higher honeydew excretion compared to West Godavari and Nalgonda populations. CO₂ + temperature levels at progressive generations have varying effects on adults and nymphs of three BPH populations. Biochemical composition of rice plants grown under different CO₂ levels revealed increased rate of soluble sugars, phenols and decreased rate of reducing sugars, soluble proteins, free amino acids, nitrogen, potassium, phosphorous in elevated CO₂ + elevated temperature levels compared to ambient conditions. Increased feeding rate of BPH under elevated CO₂ levels may be to compensate changes in host plant quality i.e., high soluble sugars and low nitrogen.

Predicting the seed cotton yield with value added medium range weather forecast data using CROPGRO-Cotton model at Bhathinda, Punjab

SANYAM — 2024-03-01

In order to assess the potential of the medium-range weather forecast in predicting the cotton productivity using crop simulation model, the CROPGRO-cotton model was calibrated and validated with the experimental data which was collected during kharif 2021 in an experiment that was carried out with two Bt cotton hybrid (RCH 776 and RCH 773) and one non-Bt (F2228), and sown at five dates i.e., April 25th, May 05th, May 15th, May 25th and June 04th in split-plot design with three replications at Punjab Agricultural University (PAU) Regional Research Station, Bathinda. The validated model was further used to assess the cotton productivity under different sowing dates using medium range weather forecast data on rainfall, maximum temperature and minimum temperature obtained for the period 2013-2021. The results showed that simulated values with medium range weather forecast were in close agreement with the simulated values for phenology and yield of cotton. The simulated cotton yield using daily medium range weather forecast data showed more or less significant efficiency to capture year-to-year as well as datewise variability in simulated cotton yield.

Trend analysis and change-point detection of monsoon rainfall in Uttarakhand and its impact on vegetation productivity

PRIYANKA SWAMI — 2024-03-01

This study analyzes the long-term spatio-temporal changes and trend analysis in rainfall using the data from 1901 to 2020 and its impact on vegetation from 2000 to 2020 across districts of Uttarakhand. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test was performed to analyze the rainfall trend. Results show that the most of the districts exhibited significant negative trend of rainfall in monsoon, except two districts. Out of 13 districts, 4 districts recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for 7 districts of Uttarakhand. Furthermore, the significant negative trend (-2.23) was recorded for overall monsoon rainfall of Uttarakhand. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the districts of Uttarakhand. A significant decline rainfall was detected after 1960 while after 1970 interannual variability of rainfall was recorded to be increased. The analysis of month wise cumulative gross primary productivity (GPP) for 13 districts with rainfall trends shows that there is significant impact of rainfall trend on GPP during month of June and it gradually reduces for subsequent monsoon months. It was observed that the GPP of region is increasing at rate of 9.1 gCm^-2d^-1 in the region since 2000. Based on sensitivity analysis, the GPP of cropped area of region is more sensitive towards rainfall than forest area of Uttarakhand.

Spatiotemporal analysis of meteorological drought in El Niño years over Oromia region, Ethiopia

GEZAHEGN MERGIA — 2024-03-01

Drought is one of the most common natural disasters globally, having major impacts on environmental, economic, and social conditions and Ethiopia is no exception particularly the Oromia region. In order to evaluate and characterize the meteorological droughts associated with El Niño years over the Oromia region, the satellite data CHIRPS was used. The monthly time series data for the period from 1991 to 2020 was used for temporal and spatial analysis of meteorological drought using standardized precipitation index (SPI) across SPI3, SPI6, SPI9 and SPI12 indices using GeoCLIM, GIS, and Python tools. The results of this study show that meteorological droughts during the El Niño years indicated an increment across weak, moderate, and strong El Niño events over the Oromia region. The dryness was visualized with frequency and duration in time-scale across short-term drought indices in time steps. The time-scale temporal meteorological drought indices in three to twelve months show that the drought indices varied in magnitude, duration, and frequency in meteorological droughts. In general, the meteorological drought severity of correlation for the remaining zones analysis between SPI3 and SPI6, SPI6 and SPI12, and SPI9 and SPI12 indices was dominated by an increment of the correlation values over short to long timescales over the study area.

Influence of weather parameters on rice false smut disease progression in Tamil Nadu, India

P. ANBAZHAGAN — 2024-03-01

False smut of rice is an upcoming menace to rice production in India. In order to understand the intricate relationship between disease incidence and weather parameters, field experiments were conducted for three years (2019, 2020 and 2021) in two cropping seasons viz., late kharif (August to November) and rabi (October to January) at the Agricultural College and Research Institute (AC & RI), Madurai, Tamil Nadu. Results revealed that the disease severity had positive correlation with relative humidity (RH), wind speed (WS) and bright sunshine hours (BSS) and negative correlation with heavy rainfall (RF), evaporation (EP) and temperature. The pooled data analysis (2019 and 2020) for the late kharif and rabi cropping season revealed that disease severity was perfectly showed positive correlation with relative humidity (0.80) and wind speed (0.83) and negatively correlated with weekly maximum temperature (-0.78) and minimum temperature (-0.84). The step wise linear regression analysis was performed which revealed that among the six weather factors minimum temperature influenced the false smut disease severity up to 92%.

Biology of pink bollworm Pectinophora gossypiella (Saunders) on cotton as influenced by temperature

KIRAN DESHMUKH — 2024-03-01

A laboratory experiment was conducted to study the biology of pink bollworm Pectinophora gossypiella (Saunders) on cotton at four different temperature levels. It was found that males have a considerably shorter total life cycle duration on cotton at 35±1°C (29.5 days) followed by 30±1°C (37.2 days), 25±1°C (46.9 days) and highest at 20±1°C (50.8 days). Similarly, minimum total life cycle duration of female P. gossypiella was recorded at 35±1°C (30.2 days) followed by 30±1°C (38.0 days), 25±1°C (47.3 days) and maximum at 20±1°C (51.7 days). The highest fecundity was observed at 30±1°C (106.2) followed by 25±1°C (100.1), 35±1°C (60.1) and lowest at 20±1°C (55.2). Male as to female sex ratio was highest at 35±1°C (1:1.5) followed by 25±1°C (1:1.4), 30±1°C (1:1.3) and lowest at 20±1°C (1:1.2). These findings revealed that variation in temperature significantly influences the life cycle duration, fecundity and sex ratio of pink bollworms on cotton, with higher temperatures accelerating development and enhancing reproductive success.

Extraction of MODIS land surface temperature and its validation over Samastipur district of Bihar

RAJESH G. M. — 2024-03-01

Trend analysis of agricultural drought and crop yield in Eastern Thrace provinces of Turkey

CAYAN ALKAN — 2024-03-01

Impact of climate change on runoff and potential evapotranspiration in Brahmani basin, Odisha, India

SONALI SWAGATIKA — 2024-03-01