Modification of microclimate is a major factor that can affect growth and productivity of French bean. A study was conducted at Ludhiana, Punjab, India during winter (2021-22) and spring season (2022) to find out the effect of microclimatic modifications using polythene sheet and shade net on production of French bean. Four treatments were formulated i.e. Control, whole season covered, covered during vegetative stage and covered during reproductive stage of two varieties (FBP-1 and Kentucky wonder) of French bean. Structures on different treatments were installed after emergence of the crop. The crop took 60-70 days to attain physiological maturity during winter season while the crop matured in 60-65 days during spring season. Higher green pod yield (167.0 q ha-1) was obtained for whole season cover conditions as compared to open conditions (130.7 q ha-1) during winter season. Pod yield was recorded less during spring season, yield under cover condition (94.8 q ha-1) was higher as compared to open condition (58.3 q ha-1). Among the both varieties FBP-1 performed better than Kentucky wonder during both the seasons. Under covered condition higher chlorophyll content along with higher vegetative & reproductive growth and earliness of crop has been observed. During spring season due to rise in temperature less yield has been obtained.

Bundelkhand region of Uttar Pradesh and Madhya Pradesh states faces various problems such as continuous drought, poor management of rainfall, soil loss, single crop focus, poverty and continuous migration of people out of the area.Agriculture in this area is based on rain fed crops such as pulses and oilseeds (Kalia et al., 2021).With its ability to tolerate dry spells and prosper in harsh soil conditions, sorghum is most suited cereal crop for Bundelkhand's semi-arid land where rainfall is uncertain, and water access is scarce.Sorghum is a vital basic food for rural families.It is extremely sensitive to temperature and photoperiod; it is important to study the role of meteorological variables to improve sorghum production and its quality.India is the third-largest producer of sorghum in the world with 6.0 million metric tons (9% of global production) after United States (14% of global production) and Nigeria (10% of global production) (IPAD 2024(IPAD -2025)).Agrometeorological indicators are vital for adapting agricultural planning, which enhances crop management and promotes food security under threat due to climate change.In the past, researchers have employed important statistical methods for crop harvesting challenges due to weather condition and its prediction on pre harvest forecasting (Garde et al., 2015;Maurya et al., 2025).Machine learning models employ algorithms to assess data, spot trends, and develop connects within the dataset, as compared to statistical models that link weather data and agricultural output using preestablished mathematical equations.Machine learning models have been successfully used to predict the yields of a number of crops including cashew, pigeon pea, rice, sorghum, coconut, wheat and soybean (Satpathi et al.,

This study presents reliable methods for estimating clear-sky land surface albedo (LSA) using machine learning (ML) and satellite data, aiming to improve climate models and environmental monitoring. Top-of-atmosphere (TOA) radiance data from the Ocean Colour Monitor-3 (OCM-3) sensor aboard the Earth Observing Satellite (EOS-06) satellite containing 13 spectral bands were used, supported by 2.4 million synthetic simulations generated via the 6S (Second Simulation of a Satellite Signal in the Solar Spectrum) Radiative Transfer Model (RTM). The simulations spanned diverse land covers, atmospheric states, sun and viewing geometries covering wavelengths from 0.4 to 2.5 µm. Three ML models namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR) were tested. Models were trained on 70% of the simulated data and tested on 30%. Validation with actual OCM-3 data included additional aerosol and water vapor information from MODIS. LSA estimations were compared to the MODIS standard product (MCD43A3). Among the three models, RF achieved the best performance, with the lowest RMSE (0.00036) and strong agreement across various land types with MODIS data. The results confer the potential of ML models, especially RF, combined with radiative simulations, and can be used for operational estimation of LSA for OCM-3 data.

A metropolitan city is a region which includes a major city and its suburbs, which is commonly characterized by its economic, social and cultural influence.Due to rapid urbanization, metropolitan cities attract many people to move to these cities for various reason such as job, education, healthcare, business etc.In 2018, the global rate of urbanization reached 55% and it is projected to 68% of the world's population will live in urban areas that by 2050, (Gupta, 2025).Climatic condition of metropolitan cities tends to change over time due to global climate change and localized anthropogenic activities such as urbanization, industrialization, pollution, rapid population growth etc (Kumar, 2021).Such activities alter land surface characteristics, increase heat absorption, and reduce evapotranspiration, contributing to the urban heat island (UHI) effect, which raises local temperatures, especially during nights (Nichol, 2005).The prolonged high temperature will lead to heatwave and heavy rainfall in a short span of time leads to urban floods and short rainfall leads to water crisis and increased dry period, these events are often occurring in these metropolitan cities and affecting the day today life of people (Matthews et al., 2017).Analysing the long-term climatic pattern is very much important for monitoring climate and policy making (Santhoshkumar et al., 2024).

Climate change presents recurring challenges in understanding extreme weather events, particularly the persistence of dry and wet periods. West Java is among the region’s most vulnerable to such rainfall variability. This study analyses the relationship between consecutive dry days (CDD) and consecutive wet days (CWD). It estimates joint return periods (JRP) using a copula-based approach to assess the spatial characteristics of climate extremes in West Java. Marginal distributions were fitted for each indicator, followed by copula modelling using the Inference Function for Margins method and model selection based on the Akaike’s information criterion (AIC). The inverse Gaussian (ING) distribution was most suitable for CDD, while the generalised extreme value (GEV) distribution best represented CWD. We found that the Gaussian and Frank copulas best captured the overall dependence structure between CDD and CWD. JRP analysis showed that simultaneous extremes (AND scheme) were significantly rarer than single-variable extremes (OR scheme). These findings provide valuable input for identifying high-risk areas and developing more locally adaptive climate risk mitigation strategies.

Climate change represents a challenge for agricultural production in Mexico, so this research determined the productive potential in the Lerma-Toluca Basin under historical (1991-2020) and future (2050 and 2070) scenarios with RCP 4.5 and 8.5. For the scenarios, temperature and precipitation images were generated using the spline interpolation method of the ANUSPLIN package. The productive potential was estimated based on the agroecological requirements of the crop, classifying the territory as high, medium and unsuitable. The results show that, in the current scenario, medium potential predominates (51.8%) over high potential (17.3%). For the future scenarios with RCP 4.5 in 2050, the medium potential increases 0.69% and the high potential 0.43%; in 2070, the medium potential decreases 0.68% and the high potential increases 1.79%. With RCP 8.5 in 2050, the medium potential decreases by 1.4%, and the high potential increases by 2.16%; in 2070, the medium decreases by 14.96%, and the high potential increases by 13.15%, compared to the historical scenario. Climate change will raise the temperature and reduce precipitation, increasing productive potential; however, extreme weather events can affect production, so adaptation strategies are required to face climate risks and guarantee food security.

The present studies were conducted at Punjab Agricultural University, Ludhiana during 2019-22. The impact of variable minimum:maximum temperature for 10:14 h, CO2 and RH on food consumption and relative growth rate (RGR) of Cnaphalocrocis medinalis was analysed. The food consumption and RGR of C. medinalis larvae were significantly influenced with change in temperature, CO2 and RH conditions. Food consumption and RGR increased with increase in temperature, CO2 and RH. The increase in temperature (22:32°C to 26:35°C), CO2 concentration (400 to 450 ppm) and RH (75 to 85 %) was found to increase the food consumption (0.0210 to 0.0450 g larva-1) and RGR (0.0200 to 0.0770 mg mg-1day-1).