Climate Research Center Research Articles

Exploring the Effective Management Strategy to Sustain Cowpea Production under High Temperature Stress

Background: Abiotic stress negatively impacts the morphological, physiological and biochemical characteristics of plants leading to loss of yield and quality. The substantial increase in population and global surface temperature extends to global food insecurity hence it is important to maintain a sustainable yield of crops. Cowpea being a protein-rich legume and a nodule-forming crop facilitates not only meeting food insecurity but also creates a sustainable environment. The objective of this study was to explore the suitable management practice for cowpea to attain sustainable yield under the elevated temperature of +2°C from ambient. Methods: An experiment in cowpea variety Co 7 was carried out in Temperature Gradient Tunnel (TGT) located at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore. The research trial was carried out during the year 2021 and 2022 with Factorial Completely Randomized Design (FCRD). In this study, we have investigated the effect of different treatments on growth, yield, quality and available nutrients in soil. Result: The application of vermicompost with foliar spray of 3% Panchagavya at 30, 45 and 60 DAS stimulated the plant height, number of leaves and leaf area index (LAI). The increase in availability of soil nutrients exhibited higher dry matter production, pod length (10.46 cm), number of seeds per pod (7.56), test weight (12.56 g), seed yield (13.25 g plant-1) and seed protein (21.82%). These results suggest that vermicompost application with 3% foliar spray of Panchagavya has a positive effect on improving the high-temperature tolerance of cowpea plants.

Minimum Temperature Forecasting Using Gated Recurrent Unit

Aim: To forecast the monthly average Minimum Temperature (ºC) in Coimbatore district.
 Study Design: Gated Recurrent Unit (GRU) has been employed to forecast the Minimum Temperature.
 Place and Duration of Study: Time series data for average month wise Minimum Temperature from January 1982 to September 2022 was collected from Agro Climate Research Centre, TNAU for Coimbatore District.
 Methodology: GRU which belongs to the field of deep learning has been employed to anticipate the average monthly Minimum Temperature by analyzing time series data from January 1982 to September 2022 in the district of Coimbatore. The model was trained using data from 1982 January to 2019 December and tested on data from 2020 January to 2022 September. After training and testing the algorithm was deployed to forecast Minimum Temperature for the lead time ahead.
 Results: The GRU model generated RMSE and MAE scores of 0.694ºC and 0.523ºC, respectively, for Minimum Temperature. GRU model had a Willmott’s Index of Agreement (WI) value as 0.943 that is very close to 1. This demonstrates the effectiveness of the model built to effectively predict the Minimum Temperature. The study's evaluation of the RMSE, MAE, and Willmott Index value made it readily evident that the GRU model performed quite accurately for forecasting Minimum Temperature. Gated Recurrent Unit algorithm was used to forecast the Minimum Temperature from October 2022 till December 2023 that is for the next 15 months.

Studies on the relationship of weather on Fall armyworm damage in maize (Zea mays L.) under different growing environments

Fall armyworm is a recently occurring invasive pest in India, the most important defoliator causing drastic damage to maize production. Hence, the present study aimed to understand the temporal infestation level of Fall armyworms on maize (Zea mays L.) with weather patterns. Field experiments were conducted during Summer (February-May) and Rainy seasons, 2022 (August-December) at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore. Three different growing environments (GE1, GE2 and GE3) were created by providing staggered sowing. Regression models were developed for per cent leaf damage against three-days lagged (LT3) and seven-day lagged (LT7) weather variables. Results showed that irrespective of growing environments, weather variables showed negative correlation (Tmax: r = -0.57, -0.81*, -0.31; SSH: -0.30, -0.48, -0.39; Tmean: -0.49, -0.23, -0.30; and SR: -0.48, -0.94*, -0.40) during summer season whereas same variables (i.e Tmax =0.62*, 0.41, 0.33; SSH = 0.09, 0.68*, 0.24; Tmean = 0.29, 0.32, 0.44; and SR=0.13, 0 .67*, 0.26 ) showed a positive correlation with PLD. Rainfall exhibits positive relation (0.06, 0.54, 0.53) and negative correlation (-0.64*, -0.10, -0.02) during summer and rainy season, respectively. Among the regression models, LT7 model had higher R2 (0.65 and 0.76) than LT3 (0.57 and 0.68) during summer and rainy seasons, respectively. These models had good regression values of 0.56 and 0.70 during Rainy and Summer, respectively. It was concluded that Tmax (32.9 °C), Tmin (23.7 °C), Tmean (28.3 °C), RH-I (85.6%), RH-II (56.4%), SSH (4.1), SR (274.6 cal cm-2 m-2), afternoon cloud cover (4.8 okta) and weekly total rainfall (10.2 mm) were very conducive for the greater leaf damage.

Assessment of AquaCrop model for simulating Baby corn (Zea mays L.) growth and productivity under different sowing windows and crop geometries

The experiments were conducted at Agro Climate Research Centre, TNAU, Coimbatore. Calibration and validation of AquaCrop model was done using Winter and Kharif, 2022 data. Calibration showed that AquaCrop accurately simulated the canopy cover by low RMSE≤13.1%, good E≤0.76, high d≤0.94 and high R2 values ≥0.98 and biomass development by low RMSE≤13.2%, high E≤0.92, good d≤0.68 and high R2 values ≥0.95. During calibration, model well-simulated the CC under second sowing (D2) and biomass under third sowing (D3). Validation showed almost good fit of CC by low RMSE≤22.0%, good E≤0.68, high d≤0.84 and high R2 values ≥0.97 and biomass development with low RMSE≤7.1%, good E≤0.66, good d≤0.60 and high R2 values ≥0.98. During Validation, model well-simulated the CC and biomass under third sowing (D3). Model showed good fit of yield during first sowing window (D1) with a less deviation for both calibration and validation (15.6% and 5.8% respectively). From the result it could be concluded that sowing windows influence on baby corn production was accurately simulated using AquaCrop during calibration (R2=0.94) and validation (R2=0.98). Hence, AquaCrop proved to be a feasible tool for maximizing the Baby corn yield under different sowing windows.

"They Don't Understand Us, but We Have to Understand Them": Interrogating the Making of Interdisciplinary Research in Chilean Climate Science.

In this article, weexamine the ways in whichthe notion ofinterdisciplinarity was understood, implemented and experienced by researchers at a government-funded Chilean climate research centre. Our multi-site ethnography, consisting of interviews, participant observations, and document analysis,was motivated by three key aims. First, to generate an inductive, multi-faceted picture of the lived meaning of "interdisciplina" at the Centre; second, to explore whether and to what extent the "peripheral" features of the research context would exacerbate the challenges associated with practicing interdisciplinarity, and third, to see whether frictions between disciplines at the Centre could be considered productive "dissonances" in Stark's sense of the term. We found that despite the centre efforts to produce a common framework to regulate interdisciplinary research,its researchers nevertheless understood, enacted and experienced it in diverse ways.More specifically, we found that researcher´s conceptions of interdisciplinarity were coloured by their lived experiences of attempting to practice it, and in particular by the benefits and costs they associated with doing so. This in turn was linked to several variables, including the specific balance between disciplines, the absence or presence of shared, clearly-defined goals, the affirmation of a common research ethic or motivational commitment, and the structural-material conditions of the research in question. We also found that the research conditions characteristic of the Global South do tend to exacerbate the well-documented challenges associated with interdisciplinarity, yet that the adversities associated with precarious conditions were often met by increased resilience and bonding among researchers, who use creative and collaborative strategies to adapt to adversity.

Scientists and climate governance: A view from the South

The importance of science for climate governance has strengthened over time and the topic inspires prolific academic writing on the influence of scientists and scientific knowledge on policy decisions. One of the streams of research in the field is inspired by Cash´s (2003) seminal work highlighting how the role of scientists depends on perceptions of salience, credibility and legitimacy. Other views call for attention to the politics involved in scientific performance while influencing policy and on the local circumstances, considering the many ways in which societies relate to science and expertise. The role of scientists in climate governance is a contested issue, relevant for many research centres aiming to influence policy decisions given the urgency of the climate crisis. To better understand this role, we reviewed mainstream international literature and identified four main approaches, which we label: scientific usable knowledge, politics of science, critical approaches and hybrid approaches. We contrasted the results with the experience of scientists from a Chilean climate research centre, to provide a view from the South on the role of scientists in climate governance. Our results show that Cash´s approach was a common ground for Chilean climate scientists, upon which they build ideas on the importance of building long-term relationships between scientists and policy makers. However, they also acknowledged the need to take into consideration the role of politics in climate-related decisions and the power relations and actor´s interests.

Thermal Time Approach under Different Sowing Windows for Greengram

Crop phenology and yield are highly influenced by prevailing weather condition during the crop growth phases. A research work was carried out at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore during June to August 2022 to determine the suitable sowing window for Greengram. The field experiment was laid out in CO 8 Greengram variety with two sowing windows of D1 - 15 June 2022 (24th SMW) and D2 - 22 June 2022 (25th SMW). The best time of sowing was established by using Growing Degree Days (GDD), Helio thermal Units (HTU), Photo thermal Units (PTU) and Heat Use Efficiency (HUE). The study revealed that the 25th Standard Meteorological week (SMW) recorded higher yield due to higher values of GDD, HTU, PTU and HUE at different crop growth stages.

​Effect of High Minimum Temperature and Enriched Night CO2 on Yield and Seed Quality of Black Gram (Vigna mungo) under Soil Plant Atmospheric Research (SPAR)

Background: Black gram is a leguminous plant species. This pulse crop has been grown on the Indian subcontinent for a long time. Black gram a drought-resistant crop that grown both in the summer and the winter, usually in rotation with rice but occasionally in mixed farming. The objective of this research was to (i) quantify the short-term effects of high minimum temperature and enriched night CO2 (HMT and enCO2) on yield contribution factors and (ii) quantify seed quality parameters employing biochemical analysis. Methods: An investigation was carried out during summer 2021 and 2022 to evaluate the effect of high minimum temperature (ambient+3°C) and enriched night CO2 (600 ppm) (HMT and enCO2) on yield and seed quality parameters of black gram (Vigna mungo) under soil plant atmospheric research (SPAR) and ambient condition at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu with ten treatments which were replicated thrice. Result: Black gram yield and quality parameters were significantly (p=0.05) reduced under HMT and enCO2. Pooled data from two successive summer seasons revealed that stress from (i) 50 to 56 DAS (Days after sowing), the number of flowers dropped per plant significantly increased by 24% and pod setting percent decreased by 19.9% (ii) 43 to 49 DAS, grain yield and biomass/plant decreased by 25.4% and 16.2%, respectively. Seed quality parameters revealed that stress during 50 to 56 DAS, seed protein, total sugar content, polyphenols, calcium and iron decreased by 19.4%, 23.7%, 16.2%, 29.6% and 30.2%, respectively. The proline and phytic acid were statistically reduced by 53.9% and 59.1%, respectively stress during 50 to 56 DAS. Stress from 64 to 70 DAS, seed moisture content was significantly decreased by 32.1%. Overall, black gram yield and seed quality was negatively affected on two treatments: stress imposed from 43 to 49 DAS (T7) and stress imposed from 50 to 56 DAS (T8) (50% flowering to pod filling stage).

A Study on Astrometeorological Relationship between Planet Azimuth and Temperature

Temperature is one of the key weather parameters, which is a necessity for all life on Earth. Any variation from the normal can impede the physical, chemical, and biological processes of life. Extremes would produce permanent changes that would halt the plant's growth and may cause complete withering. Advance information on temperature events will be helpful in protecting the plant and sustain the productivity under any temperature related disasters. Astrometeorology is one of the oldest organized knowledge systems that interplay between planetary movement and weather. In Tamil Nadu Agricultural University (TNAU), Astrometeorological weather forecast rules for rainfall, wind speed and cyclone events were already well defined. In continuation of this research, identification of Astrometeorological rules for the temperature events had been taken up during 2021-2022 at Agro climate Research Centre, TNAU, Coimbatore. Hourly temperature data from 2011-2016 was collected for 30 districts of Tamil Nadu. In each districts one particular location is selected and is correlated with ephemeris developed for a particular location using Alcyone ephemeris calculator. The findings clearly demonstrated the differential impact of individual planets and their azimuth on the temperature events. The study revealed that low temperature events were influenced when most planets are away (271- 300 degree azimuth) whereas the high temperature events were influenced by the planets that are directly above the location (91 to 120 degrees azimuth) and the in between temperature events were influenced by both 61-90 and 241-270 degrees. The specific azimuth of the Sun, Mercury, Venus, Jupiter, Saturn, Uranus, and Neptune had a makeable influence on a particular temperature event, however all the azimuths of the Moon and Mars had only a mild effect on any temperature event. Based on the results, Astrometeorological rules for the temperature events could be defined and used for the development of hybrid forecasting by overlapping the astromet forecast output on the numerical forecast output. This will produce more accuracy than individual forecast, reduce missing forecast, falls alarms, and improve the usability of forecast.

Effect of Microclimate Modification on Growth and Yield of Pearl Millet

Field experiment was conducted at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore during the summer 2022 to study the effect of microclimate modification on growth and yield of pearl millet (CO 10). Experiment was laid in Factorized Random Block Design (FRBD) with three factors viz., three levels of land configurations, two levels of mulching and two levels of water stress conditions. Totally 12 treatment combinations that replicated thrice. The results revealed that the paired row with intercropping approach produced the best growth parameters, such as plant height, leaf area index, dry matter accumulation and yield followed by paired row sowing and normal sowing due to higher solar radiation interception and utilization. In terms of irrigation level, maximum growth and yield parameters were observed with 0.75 IW/CPE ratio as compared to 0.5 IW/CPE due to lesser canopy temperature. In respect of mulch treatments, the mulched plots under both the levels of irrigation and at different land configurations had higher DMP and yield by reducing soil moisture stress, lowering the canopy temperature and higher PAR use efficiency It is concluded that the microclimatic alterations through changes in land configuration, irrigation and mulch application showed an effective adaptive strategy to mitigate adverse weather susceptibility and climatic risks in field crops.

Drought Analysis in Southern Agroclimatic Zone of Tamil Nadu using Standardized Precipitation Index

Drought differs from other natural hazards in the fact that the initiation and the termination is extremely difficult to determine and that its drastic effects continue to persist well after the cessation of the event which adds to the complexity as well as the casualties. The Southern Agro Climatic Zone (ACZ) of Tamil Nadu is heavily rain shadowed during the South West Monsoon Season, several complications arise with respect to drought, causing crop failures and depleted moisture concentration in certain cases. With that idea in perspective, a study was conducted in Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, India to study the frequency of drought over the Southern Agro-Climatic Zone of Tamil Nadu, India. Since precipitation that occurs over a region determines mainly the occurrence and cessation of drought events, precipitation-based Rainfall Deviation and Standardized Precipitation Index (SPI) was used to quantify the extent of rainfall deficit or surplus over the region. The analysis of 30 years (1991 – 2020) historical rainfall data of various districts of the Southern Agroclimatic Zone of Tamil Nadu using SPI showed that the annual SPI value ranged from +3.3 to -4. The study showed that Tirunelveli and Thoothukudi districts were most prone to moderate drought and severe drought. The region showed more frequency of Moderate drought than Severe and Extreme drought.

WRF’s microphysics options on the temporal variation in the accuracy of cluster of village level medium range rainfall forecast in Tamil Nadu

Timely and accurate medium range weather information is critical to conquer the impact of highly dynamic next few days’ weather on the farming. Advances in weather forecast models, as well as their increased resolution, have resulted in more accurate and realistic forecasts. An attempt was made during 2019 – 2021 at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore to develop cluster of village level (@ 3km resolution) Medium Range Weather Forecast (MRWF) for Tamil Nadu with higher accuracy. In this study, Weather Research and Forecast Model (WRF v4.2.1) with four microphysics viz., Kessler, WSM3, WSM5, WSM6 schemes were tested for Tamil Nadu during CWP, HWP, SWM and NEM 2020. The MRWF generated from the WRF model v4.2.1 with WSM3 had better BSF, higher Forecast Accuracy Index (FAI) and Forecast Usability Percent (FUP) for Tamil Nadu followed by Kessler scheme. The WSM5 and WSM6 were poor performer during the study. In general, CWP had higher FAI followed by HWP, NEM & SWM. The FAI from WSM3 was 0.65 - 0.74 during NEM and 0.55 - 0.69 during SWM. Among the season, the MRWF generated during SWM were over forecasted the rainfall quantity, where the NEM and HWP had better rainfall forecast nearing actuals. The FUP was higher in NEM followed by CWP, SWM & HWP, which was 57 – 88 per cent during NEM and 46 – 82 per cent during SWM. A decreasing trend in the quantitative FUP was observed with increase in lead times, irrespective of the microphysics and seasons. Finally, the study concluded that the accuracy of village level medium range rainfall forecasts from WRF model v4.2.1 varied temporally by season and the WSM3 microphysics option having superiority in all seasons.

On the use of population genomic time series for environmental monitoring.

Human-induced degradation of biodiversity is arguably the greatest problem facing humanity. Climate change, changes in land and sea use and other factors are synergistically eroding biodiversity at an unprecedented rate and scale (Díaz et al. 2019). This article is protected by copyright. All rights reserved.

Monthly rainfall hind-cast using machine learning algorithms for Coimbatore, Tamil Nadu


 Due to current world climate change, the accuracy of predicting rainfall is critical. This paper presents an approach using four different machine learning algorithms, viz., Decision Tree Regression (DTR), Gradient Boosting (GB), Ada Boost (AB) and Random Forest Regression (RFR) techniques to improve the rainfall forecast performance. When historical events are entered into the model and get validated to realise how well the output suits the known results referred as Hind-cast. Historical monthly weather parameters over a period of 42 years (1976 to 2017) were collected from Agro Climate Research Centre, Tamil Nadu Agricultural University. The global climate driver’s viz., Southern Oscillation Index and Indian Ocean Dipole indices were retrieved from Bureau of Meteorology, Australia. K- means algorithm was employed for centroid identification (which select the rows with unique distinguished features) at 90 per cent of the original data for the period of 42 years by eliminating the redundancy nature of the datawhich were used as training set. The result indicated the supremacy and notable strength of RFR over the other algorithms in terms of performance with 89.2 per cent. The Co-efficient of Determination (R2) for the predicted and observed values was found to be 0.8 for the monthly rainfall from 2015 to 2017.

Performance of High Resolution Rainfall Forecast from Weather Research Forecast Model Over Southern Agro Climatic Zone of Tamil Nadu

Weather forecasting become important tool for the planning of everyday farming activities, particularly the medium range weather forecast is highly required to reduce both input and production loss. Tamil Nadu Agricultural University is providing block level medium range weather forecast for next six days with Weather Research Forecast (WRF) Model and having accuracy of 50 -70 per cent, varies with season. An attempt was made at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore during 2019-21 to improve the accuracy of medium range rainfall forecast over different Agro Climate Zones of Tamil Nadu state by increasing the resolution from block level (9 km) to village level (3km) and altering the microphysics options in WRF v 4.2.1. The performance study tested four microphysics schemes viz., Kessler, WSM3, WSM5 and WSM6 for all the four seasons of 2020. The results obtained for the Southern Zone (SZ) is discussed in this paper and study concluded that the performance of high resolution village level (3km) rainfall forecast at Southern Agro Climatic Zone of Tamil Nadu generated from WRFv4.2.1 model was best with the WSM3 microphysics scheme followed by Kessler scheme, WSM5 and WSM6. Between the major rainfall seasons, Forecast Accuracy Index (FAI) was higher in NEM (0.56 to 0.73) than SWM (0.38 to 0.70), whereas the Forecast Usability Percent (FUP) was almost similar during both the South West Monsoon (SWM, 53.0 to 81.4) and North East Monsoon (NEM, 52.9 to 85.9). There was overestimation observed irrespective of season and microphysics options.

Exploration of Soil and Weather Factors on Mulberry Root Rot Incidence in the Western Zone of Tamil Nadu, India

Aims: To record the occurrence of mulberry root rot disease, epidemiology, interaction of weather and soil parameters with the soil-borne pathogens in Western zone of Tamil Nadu during 2019-2020.
 Study Design: Survey.
 Place and Duration of Study: Surveyed in Coimbatore, Tiruppur, Erode, Dharmapuri and Krishnagiri districts of Tamil Nadu. Laboratory experiments were carried out at Department of Sericulture & Department of Plant Pathology, Tamil Nadu Agricultural University (TNAU), Coimbatore between July 2019 and Jan 2021.
 Methodology: Per cent disease incidence of root rot was recorded in all surveyed gardens. To analyze the soil and weather parameters, the composite soil samples were subjected to textural analysis and weather data were collected from TNAU Agro Climate Research Centre. To predict soil temperature for all surveyed locations, the model regression equations were derived. The correlation analysis was done between per cent disease incidence, weather and soil parameters.
 Results: The highest disease incidence was recorded in Nallampalli block of Dharmapuri district (54 per cent) whereas the lowest in Udumalaipettai block of Tiruppur district (0.06 per cent). The infected mulberry root samples yielded complex of soil-borne pathogens including Macrophomina phaseolina, Lasiodiplodia theobromae, Fusarium sp., and pathogenicity was proved. The results revealed that root rot incidence was recorded in all types of cultivars, significantly in ruling variety V1 irrespective of its age, soil type, spacing, and irrigation method. Soil parameters like texture, temperature and moisture content were found to augment the disease. Per cent disease incidence had significantly positive correlation with the weather factors like air and soil temperature whereas negative correlation with relative humidity and rainfall.
 Conclusion: Synergism of abiotic stress factors hinders the mulberry plant health and increases its susceptibility to the soil-borne pathogens.

Dry Matter Production and Nitrogen Uptake as Influenced by Irrigation and Nitrogen Levels in Maize

A field experiment was conducted at Agro Climate Research Center, Agricultural Research Institute, P.J.T.S Agricultural University, Rajendranagar, Hyderabad, India during rabi 2019-20. The field experiment was laid out in a split plot design with three replications. The treatments comprising of three irrigation scheduling based on (Depletion of Available Soil Moisture) at 60% DASM, 40% DASM and 20% DASM as main plots and three nitrogen levels viz., 90, 180 and 240 kg N ha-1 as sub-plots. The experiment was laid out in split plot design. The results indicated that, among the different treatment combinations, the crop irrigation scheduled at 20 % DASM in conjunction with 240 kg N ha-1 accumulated significantly more dry matter of 34.2 g, 149.2 g, 233.7 g and 284.8 g plant-1, at 6th leaf, silking, dough and physiological maturity stages, respectively. The nitrogen uptake was found to be more when the crop was irrigated at 20 % DASM in conjunction with 240 kg N ha-1 (67.1 g, 231.8 g, 294.7 g and 305.3 g plant-1) at 6th leaf, silking, dough and physiological maturity stages, respectively.

Comparison Statistical Rice Yield Prediction with Multiple Weather Parameters

Advance knowledge of harvestable products, especially essential food crops such as rice, wheat, maize, and pulses, would allow policymakers and traders to plan procurement, processing, pricing, marketing, and related infrastructure and procedures. There are many statistical models are being used for the yield prediction with different weather parameter combinations. The performance of these models are dependent on the location’s weather input and its accuracy. In this context, a study was conducted at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore during Kharif (2020) season to compare the performance of four multivariate weather-based models viz., SMLR, LASSO, ENET and Bayesian models for the rice yield prediction at Tanjore district of Tamil Nadu State with Tmax, Tmin, Mean RH, WS, SSH, EVP and RF. The results indicated that the R2, RMSE, and nRMSE values of the above models were ranged between 0.54 to 0.79 per cent, 149 to 398 kg/ha, 4.0 to 10.6 per cent, respectively. The study concluded that the Bayesian model was found to be more reliable followed by LASSO and ENET. In addition, it was found that the Bayesian model could perform better even with limited weather parameters and detention of wind speed, sunshine hours and evaporation data would not affect the model performance. It is concluded that Bayesian model may be a better option for rice yield forecasting in Thanjavur districts of Tamil Nadu.

Effect of Plant Growth Regulators on Cowpea (Vigna unguiculata (L.) Walp) Productivity under High Temperature Stress

High temperature stress severely interrupts the physiological and biochemical functions in plants which hinders the growth, development and productivity of cowpea. The present study evaluates the potential roles of Naphthalene Acetic Acid and Brassinolide for mitigating the adverse effects of high temperature in cowpea. The study was carried out during the year 2021 in cowpea variety CO 7 using Temperature Gradient Tunnel (TGT) located at Agro Climate Research Centre, Tamil Nadu Agricultural University (TNAU), Coimbatore. The experimental design followed was Factorial Completely Randomized Design (FCRD). Different treatments were imposed on plants exposed to ambient and ambient +2oC temperature. The effect of treatments on leaf area index, photosynthetic rate, chlorophyll index and seed yield were studied. The application of brassinolide @ 0.2ppm showed a positive effect on stressed plants and produced the highest seed yield of 8.78 g plant-1.

A Model Interdisciplinary Collaboration to Engage and Mentor Underrepresented Minority Students in Lived Arctic and Climate Science Research Experiences

The Polaris Project, a National Science Foundation–funded program at the Woodwell Climate Research Center, aims to comprehensively address minority participation in climate and Arctic science research. Critical participant outcomes included development of interdisciplinary research projects, involvement in self-efficacy and advocacy experiences, and increased awareness and discussion of Arctic research careers.