Over the decade, rapid climatic changes threaten agroecosystem stability and food security. The rapid transition from natural vegetation to agricultural land results to alteration of surface energy balance. Numerous interactions occur within the agroecosystem among its diverse components. Properly understanding these interactions helps mitigate environmental impact through modern climate-smart technologies and sustainable crop water management based on unique needs. Smart water management paves the pathway, particularly in water crises phase through the use of numerous contemporary artificial intelligences, machine learning tools, agrometeorological models and Internet of Things based modern watering devices aid in the efficient use of resources both on and off farm to improve agricultural output and quality. The study's objective is reviewing core technologies including advanced sensors, internet of things, remote sensing and agrometeorological models. The highlights of this study to investigate SWM systems. A comparative analysis of existing technologies identifies challenges such as high cost, data privacy concerns and policy gap. To address these gaps, this study proposes an integrated approach that combines artificial intelligence, remote sensing and IoT framework as most effective approach, enabling real time monitoring, precise irrigation scheduling and adaptive response to climate variability. Advancing these technologies with suitable, cost effective solutions, and policy interventions is crucial for ensuring climate resilient, increasing the efficiency of the smart management system and sustainable agricultural water management.

The rising global population and climate change demand sustainable weed management to ensure food security. Conventional herbicides face limitations due to resistant weed species, land use changes, and environmental concerns. Nanoherbicides offer promising solutions through targeted delivery, reduced residues, and enhanced efficacy. A scientific evaluation of their mechanisms, benefits, and limitations is essential to support their integration into sustainable agriculture. This article explores the potential of nanoherbicides as a sustainable alternative to conventional herbicides, emphasizing their role in precision weed management through nanocarrier systems and controlled-release formulations. It highlights how organic and inorganic nanocarriers enable targeted delivery, minimize environmental damage, and improve herbicide efficacy while reducing chemical runoff and soil/water contamination. The review also discusses the ecological and regulatory concerns, stressing the need for standardized global guidelines and multi-tiered risk assessments to ensure environmental and human safety. It presents strategies for minimizing these risks by utilizing eco-friendly nanomaterials and context-specific controlled-release systems integrated with advanced monitoring protocols. From this study, nanoherbicides emerge as a sustainable and efficient alternative to conventional herbicides, offering targeted weed control with minimal environmental disruption. Nanocarriers enable controlled-release formulations that reduce chemical runoff and improve herbicide efficacy. Despite their benefits, concerns remain about bioaccumulation, toxicity to non-target organisms, and long-term effects on soil health. A multi-tiered risk assessment framework is essential to ensure environmental safety and effective regulation. Advancements in smart delivery systems and encapsulation technologies hold promise for future precision weed management. Nanoherbicides offer a promising approach to sustainable weed management, in line with the global push for environmentally friendly agricultural practices and food security. Graphical abstract The alternative text for this image may have been generated using AI.

Evapotranspiration (ET) is a key variable in the hydrologic cycle that directly affects the redistribution of precipitation and surface balance. ET measurements with high temporal resolution are required for coupling with models of highly dynamic processes, e.g., hydrological and land surface processes. The Amaravathi River Basin is the focus of Tamil Nadu, as its major water resource for industry and major grain producing regions. However, this area is facing serious water resource shortages and water pollution problems. The present study used Landsat 8 satellite remote sensing data, in situ meteorological observations, and the surface energy balance algorithm for land (SEBAL) model with daily vegetative indices and energy fluxes in the River Basin. Moreover, factors influencing ET were also identified based on the results of this study. ET varies with land cover type and physical and chemical properties of the underlying surface. Furthermore, ET is also controlled by water availability, radiation, and other atmospheric weather factors conditions, whereas the CROPWAT model, based on the FAO 56 Penman-Monteith method, was employed to estimate seasonal water requirements and comparison was done with the SEBAL model. The root mean square error (RMSE) for ETa exhibited an RMSE of 0.65 mmd ⁻¹ and demonstrated an R ² exceeding 0.93. Finally, based upon crop cutting experiments (CCE), yield data and SEBAL-derived ET spatial water productivity maps were obtained by mathematical operations of the yield and ET _a raster maps. The average maize yield of the study area was 7,567 kg ha ⁻¹ , which was close to official agricultural marketing department data. The seasonal maize crop water requirement based upon SEBAL was 470 mm and that based upon CROPWAT was 506.3 mm, indicating that the SEBAL model shows good capability to detect CWR demand under study at the Amaravathi River basin scale. The average physical and economic maize water productivity was 1.61 kg m ⁻³ and Rs. 40 m ⁻³ across the Amaravathi River Basin, respectively. This study identifies high-performing and low-performing water productivity regions across the Amaravathi River Basin.

Evapotranspiration (ET) is a key component of the hydrological cycle and climate change has impacted its patterns, potentially leading to abnormal weather conditions. This study investigates the trends in reference evapotranspiration (ET0) over the Amaravathi river basin using monthly ET0 of AgERA5 dataset from 1979 to 2022. The result revealed that the mean annual ET0 was 1909.79 mm with a low coefficient of variation (CV) of 3.14 %, indicating stability despite seasonal fluctuations. The Southwest Monsoon (SWM) accounted for the largest share (36.20 %) of annual ETo, followed by summer (30.30 %), Northeast Monsoon (NEM) (18.48 %) and winter (15.02 %). Trend analysis using the Mann-Kendall test and Sen's slope estimation showed a significant decreasing trend in annual ET0 at a rate of 2.35 mm per year (p < 0.05), suggesting a long-term decline in evapotranspiration potential. Pettitt’s test identified a significant change point in the ET0 series, with a shift around 2003, indicating a change in the climatic regime. Principal Component Analysis (PCA) further corroborated these trends, with the first principal component (PC1) explaining 63.24 % of the variance, strongly correlating with ET0, solar radiation and mean temperature. The significant drop in ET0 over the previous few decades is highlighted in this study, highlighting the necessity of adaptive water management techniques considering shifting climatic conditions.