Journal of Agrometeorology

Assessing future precipitation and temperature changes for the Kesinga Basin, India according to CORDEX-WAS climate projections

2023-06-05T00:46:50+00:00

Assessment of growth and productivity of pearl millet (Pennisetum glaucum L.) with varied sowing environments and nitrogen concentrations using AquaCrop model

2023-06-11T06:54:37+00:00

Calibration and validation of APSIM maize simulation model for different date of sowing

2023-05-04T12:23:50+00:00

Estimation of actual evapotranspiration using the simplified-surface energy balance index model on an irrigated agricultural farm

2023-07-01T00:35:40+00:00

Evapotranspiration (ET) plays a crucial role in the energy and water balance of agricultural ecosystems and is a vital component of the hydrological cycle. Efficient irrigation water management relies on accurate spatiotemporal coverage of crop ET across a farm. Thanks to the availability of multi-temporal high-resolution satellite datasets and remote sensing-based surface energy balance models, near-real-time estimation of ET is now possible. This study utilized Landsat 8/9 data to estimate ET using the simplified surface energy balance index (S-SEBI) model, which was then compared to eddy covariance measurements over a semi-arid agricultural farm in New Delhi, India during the post-monsoon periods of 2021-22 and 2022-23. The S-SEBI model predicted daily ET from Landsat 8/9 data with an average correlation coefficient and RMSE of 0.89 and 0.79 mm/day, respectively. The spatiotemporal map was also used to evaluate the model's performance, and it could accurately differentiate between ET over dryland crops and well-irrigated wheat fields on the farm. Despite underestimating ET (0.51 mm/day) during the initial growing season (Nov-Dec) and overestimating it (0.73 mm/day) during mid-season (Feb-Mar), the S-SEBI model can still be an operational tool for mapping ET with high accuracy and sufficient variation across pixels, making it an ideal option for incorporating into irrigation scheduling.

Heat wave characterization and its impact on carbon and water vapour fluxes over sugarcane-based agroecosystem

2023-07-12T08:55:04+00:00

Global climate change expected to exacerbate the temperature extremes and intensity of heat waves in recent decades. The terrestrial biosphere plays a crucial role in absorbing carbon from the atmosphere. Therefore, understanding how terrestrial ecosystems respond to extreme temperatures is essential for predicting land-surface feedbacks in a changing climate. In light of this, a study was conducted to assess the effects of 2022 heat wave [March-May (MAM)] on carbon and water vapour fluxes. This study utilized the measurements obtained from the eddy covariance tower mounted within the sugarcane agroecosystem. The study period (MAM) was characterized into three events: Heat wave event 1 (HE1), Heat wave event 2 (HE2), Non heat wave event (NHE). The variation in carbon and water vapour fluxes, along with meteorological variables, during these events in 2020 and 2022 was further analysed. Our findings indicate that the heat wave caused a decrease in net ecosystem exchange (NEE), leading to an increase in atmospheric CO₂ concentration during HE1, HE2 compared to NHE. In HE1, maximum NEE in 2020 and 2022 was -19.15 µmol m^-2 s^-1 and -13.21 µmol m^-2 s^-1, respectively. Furthermore, the heat wave events led to a decrease in latent heat flux (LE) and sensible heat flux (H), with changes of up to 5% in LE and 57% in H compared to the same period in 2020. These results highlight the significant impact of the heatwave on both carbon and energy fluxes. Overall, the present study provides a valuable reference for further climate change analysis, specifically focusing on both carbon and energy fluxes within sugarcane ecosystem.

Interactive effect of tillage, residue, nitrogen, and irrigation management on yield, radiation productivity and water productivity of winter wheat in semi-arid climate

2023-06-27T00:35:44+00:00

Water, nutrients, and energy are the three main inputs in agricultural production and recently there has been a drop in the factor productivity of these inputs because of their improper management and deterioration of soil health. To maximize agricultural productivity while lowering strain on natural resources, the best synergistic combinations of tillage, residue, nitrogen, and water management should be identified for improving resource use efficiency of wheat. Hence, an attempt has been made to evaluate the impact of contrasting tillage, crop residue mulch, nitrogen, and irrigation interaction on yield, radiation productivity (RP), and water productivity (WP) of wheat in a split-factorial design. Results showed that wheat yield was higher under no-tillage (4.8%) than that of conventional tillage. Crop residue mulch (CRM) and higher nitrogen application enhanced RP, WP, and yield of wheat; although RP increased with increase in nitrogen application up to 100% recommended dose of nitrogen (RDN). CRM significantly reduced the seasonal evapotranspiration (6.0‒7.2%) as compared to residue removal treatment. Deficit irrigation enhanced the WP while it lowered the crop yield significantly. Therefore, wheat can be grown under no-tillage, CRM, 100% RDN with deficit irrigation to obtain higher WP but with full irrigation to obtain higher yield, and RP in the semiarid climate of India.

Comparative study of water requirements and water footprints of fibre crops hemp (Cannabis sativa) and cotton (Gossypium hirsutum L.)

2023-07-15T12:03:54+00:00

Water is a valuable and limited resource, which is becoming increasingly under pressure due to the impacts of climate change and over utilization by the agricultural industry. Cotton is the predominant natural fibre utilized within textiles and is a highly water-intensive crop, thereby contributing to the negative environmental impacts of water use in agriculture, such as depletion of water from ecosystems and other uses, land degradation, and dissemination of pollutants. Accordingly, there is significant interest in establishing alternative natural fibre sources, which have lower water requirements. Cannabis sativa (hemp) fibre is becoming an increasingly popular fibre alternative and is purported to require less water during its cultivation. Accordingly, herein data was compared across 28 prior published sources, which identified that hemp has a 38% lower crop water requirement (CWR), 60% lower water footprint (WF), 84% lower crop irrigation requirement (CIR), and 91% lower irrigated water footprint (IRF) as compared to cotton. Therefore, these results support hemp as a water-efficient environmentally sustainable alternative to cotton for fibre cultivation.

Climate change impacts on water flux dynamics in Shingoda basin having agriculture and forest ecosystems: A comprehensive analysis

2023-07-23T06:00:00+00:00

An assessment of climate chnage and its impacts on water fluxes in the Shingoda basin of the Saurashtra region having 14% agriculture and 75% forest were made through analysis of time series (1951-2100) of bias corrected maximum/minimum temperature and rainfall (RCP4.5), rreference evapotranspiration (ET_o), evapotranspiration (ET_c) and runoff. Results showed significant climate changes in the basin, with day mean temperature rising from 24.4°C in the second half of the 20^th century to 26.5°C and 27.9°C in the first and second half of the 21^st century, respectively. During the first and second half of the 21^st century, seasonal rainfall increased by 23.0% and 46.33%, and runoff rose by 46.78% and 86.40% compared to the second half of the 20^th century. However, annual reference evapotranspiration (ET_o) decreased by -1.41% and -6.5%, and crop evapotranspiration (ET_c) decreased by -3.2% and -9.8% in the same periods. The analysis also revealed a deficit of -16.10% in downward water flux (rainfall) in the first half of the 20^th century, followed by a surplus of 8.46% and 28.37% compared to the upward flux (ET_c) in subsequent periods. The upward water flux deficit during 2^nd half of 20^th century were supported by evidence of depleted groundwater levels and seawater intrusion in the study area.

Co-elevation of atmospheric CO2 and temperature affect instantaneous and intrinsic water use efficiency of rice varieties

2023-06-28T02:34:13+00:00

Greenhouse gas (GHG) emissions from anthropogenic activities are the most significant drivers of climate change, which has both direct and indirect effects on crop production. The study was conducted during the kharif season for two years inside the Open Top Chamber (OTC) at the Genetic-H field of ICAR-Indian Agriculture Research Institute (IARI) to quantify the effect of elevated CO₂ and temperature on water use efficiency of rice varieties. There were two different CO₂ concentrations i.e. ambient (410 ppm) and elevated (550 ± 25 ppm) and also two different temperature levels i.e. ambient and elevated (+2.5-2.9°C). Results suggested that warming caused more accumulated GDD in rice and which negatively affected the duration of both the varieties. In elevated CO₂plus high temperature interaction treatment net photosynthesis rate was more than that of chamber control. Stomatal conductance and transpiration rate reduced with co-elevation of CO₂ and temperature. Co-elevation of CO₂ and temperature, has also improved WUE (both instantaneous and intrinsic) through enhanced carbon assimilation and reduced stomatal conductance, thus, reducing the amount of water lost through transpiration, eventually improving WUE of the crop.

Climate change impact on potato (Solanum tuberosum) productivity and relative adaptation strategies

2023-06-22T12:28:48+00:00

WOFOST and InfoCrop crop growth simulation models were used to assess the impact of climate change on potato cultivars and to develop adaptation strategies for future climatic scenarios (2030, 2050 and 2080) under representative concentration pathways (RCP’s) 4.5 and 6.0 in Bihar. Potato cultivars belonging to late (Kufri Badshah), medium (Kufri Jyoti) and early (Kufri Pukhraj) maturity groups were selected. The simulated results revealed variations in potential productivity of potato under both RCP’s (4.5 & 6.0) with baseline yields of 43.80 t ha^-1 for Kufri Badshah, 41.5 t ha^-1 for Kufri Jyoti and 43.6 t ha^-1 for Kufri Pukhraj. Under RCP 4.5, elevated concentration of CO₂projected to increase the productivity of Kufri Badshah, Kufri Jyoti, and Kufri Pukhraj. However, a decline in yield is expected when individual effect of temperature is considered for future climatic scenarios (2030, 2050 & 2080). However, these yield loss is negated when combined effect of CO₂ and temperature is considered by 1.3, 0.7 and 0.3 % in 2030, by -0.4, -1.1 and -2.2 % in 2050 and by 3.5, 4.4 and 5.9 % in 2080, respectively. Likewise, for RCP 6.0, combined effect of CO₂ and temperature offset the yield losses by 2.6, 2.4 and 2.3% in 2030, 2.1, 1.7 and 1.1 in 2050 and 1.1, -0.1 and -1.8 in 2080. In addition, selection of suitable cultivars, shifting the date of planting and proper irrigation and nitrogen management practices can counterbalance the yield losses.

Response of stress irrigation management on chlorophyll content, water potential, PAR and canopy temperature in tomato (Lycopersicum Esculentum Mill.)

2023-04-13T14:02:26+00:00

This study was conducted to investigate the response of stress irrigation management on chlorophyll content, water potential, photosynthetically active radiation (PAR) and canopy temperature in tomato during summer season. The main plot treatments consist of three drying cycles that is 7, 11 and 15 days and sub treatments include four irrigation levels viz.,60, 80, 100, and 120 % ET_C. The control treatments i.e. drip irrigation with 100% ET_C on every two alternate days. The results showed that the 7 days drying cycle showed maximum chlorophyll content, absorbed PAR and leaf water potential followed by 11 days drying cycle. Among the drip irrigation levels, the maximum drip irrigation levels 120 % ET_C exhibited significantly maximum chlorophyll content, absorbed PAR and leaf water potential. However, it was at par with 100 % ET_C and further 80 % ET_C drip irrigation level also showed significant at 90 and 120 DAT. While in the case of difference between canopy and air temperature (Tc-Ta) less negative values were noted by 7 days drying cycle and 120% ET_C drip irrigation level.

Comparison of phenological weather indices based statistical, machine learning and hybrid models for soybean yield forecasting in Uttarakhand

2023-07-01T13:23:50+00:00

Early information exchange regarding predicted crop production could play a role in lowering the danger of food insecurity. In this study total six multivariate models were developed using past time series yield data and weather indices viz. SMLR, PCA-SMLR, ANN, PCA-ANN, SMLR-ANN and PCA-SMLR-ANN for three major soybean producing districts of Uttarakhand viz. Almora, Udham Singh Nagar and Uttarkashi. Further analysis was done by fixing 80% of the data for calibration and the remaining dataset for validation to predict soybean yield. Phenology wise average values were computed using the daily weather data. These average values are subsequently employed in the computation of both weighted and unweighted weather indices. The PCA-SMLR-ANN, SMLR-ANN and PCA-ANN models were found to be the best soybean yield predictor model for Almora, Udham Singh Nagar and Uttarkashi districts, respectively. The overall ranking based on the performances of the models for all locations can be given as: SMLR-ANN > PCA-ANN > PCA-SMLR-ANN ≈ ANN > PCA-SMLR > SMLR. The study results indicated that hybrid models outperformed the individual models well for all the study regions.

Comparative evaluation of penalized regression models with multiple linear regression for predicting rapeseed-mustard yield: Weather-indices based approach

2023-06-12T11:07:13+00:00

Rapeseed-mustard (Brassica spp.) is one of the important edible oilseeds crops in India. The same level of weather condition impacts the growth and establishment of rapeseed-mustard plant differently in different stages of crop which lead to large intra-seasonal yield variations. Hence it is essential to give weightage to weekly weather conditions while fitting predictive model. In this present study, path-coefficient based weighted index was proposed along with existing unweighted and correlation based weighted index. The performance of penalized regression models viz. Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ENET) were compared with Multiple Linear Regression (MLR) for predicting rapeseed-mustard yield using weather-indices. The results revealed that the path-coefficient based weighting of weather parameters to the yield were stable than correlation based weighted-indices. Path-coefficient based weighted indices of maximum temperature, minimum temperature and windspeed were important variables in projection of yield. The performance of MLR was poor during validation of model due to overfitting issue. The performance of penalized models was stable in both calibration and validation of the model. The LASSO and ENET models that accompanied with coefficient shrinkage and variable selection were found to be the best fitted models for predicting Rapeseed-Mustard yield.

Development of groundnut yield forecasting models in relation to weather parameters in Andhra Pradesh, India

2023-05-28T10:00:00+00:00

Groundnut is a key oilseed crop in the world and India is one of the largest groundnuts producing country in terms of area and yield. Keeping that in view, five models were developed for five districts of Andhra Pradesh to forecast the groundnut yield viz., Stepwise Multiple Linear Regression (SMLR), Ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ELNET) and Artificial Neural Network (ANN). The historical data on the weather parameters are obtained from NASA POWER web portal and groundnut yields for these districts of the state during both Kharif and Rabi seasons obtained through Season and Crop Report, Government of Andhra Pradesh for the period, 2001 to 2020. In total 30 weather indices were generated through five weather variables. The assessment of models was done by fixing 75 % of the data for calibration and left 25 % data for validation. The findings inferred that based on the values of R², RMSE, nRMSE and EF, Ridge regression, ELNET and ANN models showed better performance for Ananthapur, Chittoor and Kadapa districts and SMLR and LASSO models showed better performance for Kurnool and Nellore districts during both Kharif and Rabi seasons at calibration and validation stages.

Heat unit requirement of sweet corn under different planting methods and dates in temperate Kashmir, India

2023-07-16T10:10:05+00:00

In order to investigate the "Effect of Establishment method and Planting date on phenology, yield, and agrometeorological indices for sweet corn," a field experiment was carried out at the Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir Wadura, Sopore experimental farm of the Division of Agronomy, over the course of two sessions in Kharif 2020 and 2021. The experiment had two components: a distinct sowing date with a 20-day interval and two establishment methods (direct seeding and transplanting). The initial planting day was (25th of April, 2nd was 15th of May and third was 5th of June during both the years) Three replications in RCBD were confirmed. Following transplanting with the first date of sowing, direct seeding required the most days to attain different phenological stages and accumulate the most heat units. Transplanting with the initial date of sowing resulted in noticeably greater HUE, HTUE, PTUE, and HyTUE, resulting in the largest green cob and biological yield as compared to other dates of sowing and direct seeding. As a result, given the weather in Kashmir It was discovered that planting on the first day of sowing increased sweet corn yields economically.

Relationship between hydrothermal coefficient (HTC) and productivity of pastures in the arid zone of Northwestern Caspian Sea

2023-06-21T04:24:15+00:00

In the arid zone, one of the ways to provide animals with feed is the organization of forested pastures, the productivity of which largely depends on weather conditions. Our study analyzes changes in meteorological conditions and hydrothermal coefficient (HTC) during the growing season April-October from 2018 to 2022 and their impact, on natural and forest-reclaimed pastures of the sandy Bazhigan massif of Northwestern Caspian Sea. Pasture productively was negatively correlated with the temperature and positively correlated with the precipitation. The relationship between hydrothermal coefficient (HTC) and productivity of different types of pastures has been established with coefficient of determination of R² of 0.765 under pasture with different density and R² of 0.879 under natural pasture. Results showed that the atmospheric humidification is the determining factor of stable pasture productivity in the conditions of climate change in the arid zone of Russia.

Population dynamics of aphid and their natural enemies in mustard based on meteorological parameters using principal component analysis

2023-06-18T11:12:08+00:00

An experiment was conducted at the research farm of the Rajasthan Agricultural Research Institute, Durgapura, Jaipur, during Rabi, 2020–21 and 2021–22, to study the impact of meteorological parameters on the populations of the aphid, Lipaphis erysimi (Kalt) it’s associated natural enemies coccinellids, Coccinella septempunctata and syrphid flies, Xanthogramma scutellariae. The correlation coefficients with the pooled data, showed a substantial negative correlation of aphid population with temperature (r = -0.466 and -0.582*) as well as with average relative humidity (r =0.489*). C. septempunctata and X. scutellariae had positive significant correlations with L. erysimi (r = 0.965* and 0.988* respectively). The most significant variables for aphid populations, according to PC1 and PC2 (initial components of principal component analysis), are biotic factors and weather parameters.

Effect of abiotic factors on pathotypes causing yellow and brown rust in wheat

2023-04-06T07:03:01+00:00

An attempt was made to determine the most favourable abiotic factors influencing germination of urediniospores of different pathotypes of Puccinia species. The causal organism of rusts in wheat is Puccinia spp. Five pathotypes of Puccinia striiformis (46S119, 78S84, 110S84, 110S119, 238S119) causal organism of yellow rust and two pathotypes of Puccinia triticina (77-5 and 77-9) causal organism of brown rust in wheat were obtained from Department of Plant Pathology, Punjab Agricultural University, Ludhiana. The data related to spore germination on agar slides was analysed and the levels of urediniospores germination at different temperatures (5,10,15 and 20^oC) and pH (5,6,7 and 8) for each pathotype was compared using analysis of variance. The most appropriate temperature and pH were later used to conduct an experiment to study effect of different light intensities (500, 750,1000 and 1250 lux) on spore germination of all the pathotypes under study. The data showed that on agar, 15°C was proved as most suitable for urediniospore germination for Puccinia striiformis. Mean per cent spore germination was highest over the temperature range 15°C (43.55%) for Pst pathoypes and dropped significantly at 10°C (37.97%), 20°C (29.66%) and 5°C (21.04%). Mean urediniospore germination for Puccinia triticina was highest at 20°C (43.89%) followed by 15°C (39.44%), 10°C (30.43) and 5°C (27.39% ). Experimental results revealed that per cent spore germination was better under pH 7 followed by 6, 5 and 8 for all the pathotypes considered for study. The highest urediniospore germination was observed for 1250 lux (46.54%) followed by 1000 lux (41.29%), 750 lux (38.42%) and 500 lux (27.60%).

Exploring the landscape of contemporary crop micrometeorology: A bibliometric investigation

2023-08-11T11:52:36+00:00

Micrometeorology plays a pivotal role in advancing our understanding of agricultural systems by unraveling intricate interactions between climate dynamics and crop performance. This article presents a comprehensive analysis of the literature published on crop micrometeorology and indexed in Scopus database from 2000 to 2023. The query yielded only 146 documents, which were subsequently subjected to analysis using an R-based bibliometric tool to assess annual scientific production trend, document types, citation, and keyword analysis. The results revealed zero growth rate of the topic with an average 47.36 citations and total citation of 6536 in the analysis period. USA dominates the number of publications (28.1%), followed by China (17.8%), Japan (11.6%) and Australia (8.9%). India stood at 10^th position with only 8 documents contributing 5.5% of the total publications included in the study. The key domains of current research in the realm of crop micrometeorology identified through bibliometric analysis were evapotranspiration, energy balance, gas emissions, and modelling based studies, which are discussed in details in the article. As climate change and global food security becomes more critical, this analysis highlights the role of micrometeorological works within the realm of climate change and crop studies.

Advancements in remote sensing based crop yield modelling in India

2023-08-10T11:14:29+00:00

Crop yield prediction at regional levels is an essential task for the decision-makers for rapid decision making. Pre-harvest prediction of a crop yield can prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. With the advent in digital world, various advanced techniques are employed for crop yield prediction. Remote Sensing (RS) data with its capability to provide the synoptic view of the Earth’s surface, has numerous returns in the area of crop monitoring and yield prediction. This study provides as a review for the advanced techniques for crop yield prediction in India with RS data as a base. The advanced techniques like RS based statistical yield modelling, machine learning based yield modelling, semi-physical yield modelling are described in the current study. The assessment of the studies related to integration of RS data in crop simulation model is also described in a section. All the techniques involved in the current study show significant improvements in crop yield prediction, enabling the development of new agricultural applications in India.

Climate change and agricultural ecosystem: Challenges and microbial interventions for mitigation

2023-08-11T03:02:53+00:00

Climate change has an impact on agricultural activity because of its direct reliance on climate change. There are two types of relationships between agriculture and climate change, and they are extremely important, particularly for developing and underdeveloped or low-income countries, which rely heavily on agriculture for subsistence and lack adaptation infrastructure when compared to developed countries. Geographically high-latitude places that already have low temperatures might benefit from a prolonged growing season when temperatures rise due to climate change. GHG emissions such as carbon dioxide, nitrous oxide, and methane have an impact on agricultural lands. Gases have an impact on climate through emitting greenhouse gases. Emissions are mostly caused by tillage operations, fossil fuels, fertilized agricultural soils, and farm animal waste, and have a significant impact on the agriculture industry. Agriculture, on the other hand, might be a solution to climate change by lowering emissions and extensively implementing mitigation and adaptation measures. Best management approaches such as use of microbial inoculants to reduce fertilizer inputs, carbon sequestration and methane oxidation has potential to reduce greenhouse gases from agro-ecosystem.