Agricultural Advisory Diagnostics Using a Data-Based Approach: Test Case in an Intensively Managed Rural Landscape in the Ganga River Basin, India

technology adoption through agricultural extension may be a consequence of providing generic information without sufficient adaptation to local conditions. Data-rich paradigms may be disruptive to extension services and can potentially change farmer-advisor interactions. This study fills a gap in pre-existing, generic advisory programs by suggesting an approach to “diagnose” farm-specific agricultural issues quantitatively first in order to facilitate advisors in developing farm-centric advisories. A user-friendly Farm Agricultural Diagnostics (FAD) tool is developed in Microsoft Excel VBA that uses farmer surveys and soil testing to quantify current agricultural performance, classify farms into different performance categories relative to a localized performance target, and visualize farm performance within a user-friendly interface. The advisory diagnostics approach is tested in Kanpur, representative of an intensively managed rural landscape in the Ganga river basin in India. The developed open-source tool is made available online to generate data-based agricultural advisories. During the field testing in Kanpur, the tool identifies 24% farms as nutrient-limited, 34% farms as water-limited, 27% farms with nutrient and water co-limitations, and the remaining farms as satisfactory compared to the localized performance target. It is recommended to design advisories in terms of water and nutrient recommendations which can fulfill the farm needs identified by the tool. The tool will add data-based value to pre-existing demand based advisory services in agricultural extension programs. The primary users of the tools are academic, governmental and non-governmental agencies working in the agricultural sector, whose rigorous scientific research, soil testing capacity, and direct stakeholder engagement, respectively, can be harnessed to generate more data-based and customized advisories, potentially improving farmer uptake of agricultural advisories.

Estimation and Prediction of Riverbank Erosion Susceptibility and Shifting Rate Using DSAS, BEHI, and REBVI Models: Evidence from the Lower Ganga River in India

The process of riverbank erosion (RE) is often accelerated by natural events and anthropogenic activities leading to the transformation of this natural process to natural hazard. The present study aims to estimate bank erosion rate and prediction of the lower Ganga River in India using digital shoreline analysis system (DSAS) model. The prediction of RE susceptibility mapping has been generated using three ensemble models such as DSAS, bank erosion hazard index (BEHI), and river embankment breaching vulnerability index (REBVI). For the study satellite images and field data (bank materials, geotechnical parameters, embankment structure, hydraulic pressure etc.) have been used to recognize the river bank position and BEHI and REBVI scores. During 1973-2020, the average bank erosion and accretion rate was found 0.059 km/y and 0.022 km/y at the left bank while 0.026 km/y and 0.046 at the right bank respectively. The prediction results illustrated that the very high vulnerable condition of 06 villages and 21 villages for high vulnerable due to left bank erosion. BEHI and REBVI scores have been the significant performance of understanding and identification of RE vulnerable areas. The long-term (2020-2045) average erosion and deposition rate was predicted at 0.135 km/y and 0.024 km/y at the left bank and 0.043 km/y and 0.045 km/y at the right bank respectively. The prediction accuracy and validation of models were measures by statistical techniques such as student’s t-test, RMSE, and R2 values. This study would be help planners and decision makers the spatial guidelines to understanding future trends of bank erosion and shifting rate for land-use planning and management strategies to protect riverbank.

First record of exotic alligator gar, Atractosteus spatula (Actinopterygii: Lepisosteiformes: Lepisosteidae), from Ganga River system, India: A possible threat to indigenous riverine fish diversity

A new record of an exotic alligator gar, Atractosteus spatula (Lacepède, 1803), from an open wetland of the Ganga River was presented in this paper and discussed along with the environmental parameters. Entry of the exotic fish into the natural system was probably a result of uncontrolled ornamental fish trading. Considering threats of this predatory fish to become invasive and disturb riverine fish diversity, possible ways to avoid such risk have been discussed.

Diversity of aquatic insects and biomonitoring of water quality in the upper Ganga River, a Ramsar site: a preliminary assessment

Monitoring of freshwater habitats through aquatic insects is widely used. A study was carried out in March, 2019 at 14 sites in the Upper Ganga River between Brijghat and Narora, a riverine Ramsar site in India, to document the diversity of three major aquatic predatory insect groups—Odonata, Coleoptera, and Hemiptera—and determine their biomonitoring potential. The study recorded three species of Coleoptera, four Hemiptera, 14 dragonflies, and eight damselflies. The Shannon diversity index (H′) ranged from 2.465 to 2.782, Pielou’s Evenness index (J′) from 0.841 to 0.894, and Berger–Parker index of dominance (d) from 0.122 to 0.243. Families Libellulidae (Odonata), Coenagrionidae (Odonata) and Gerridae (Hemiptera) had high relative abundance and dominant status. The stream invertebrate grade number-average level (SIGNAL2) score (for family) ranged from 2.316 to 3.174, lying within quadrant 2 of the SIGNAL2 (family) quadrant diagram. This suggested that the water in the area is likely to have high levels of turbidity, salinity, or nutrients, caused naturally or by anthropogenic activities, and the water has low levels of most toxic chemicals.

Hydrometeorological Consequences on the Water Balance in the Ganga River System Under Changing Climatic Conditions using Land Surface Model

United Nations Sustainable Development Goal (SDG) ensures adequately accessible water and management for all. Due to the rapid increase in population and industries along the Ganga river, it is necessary to estimate the water budget for fulfilling the demand for water in the future. The M-K test conducted on the Noah-Land Surface Model data for 72 years results in maximum declining trend of water budget in the Yamuna Lower (Q=-3.82BCM/year), and minimum in the Damodar sub-basin (Q=-0.10BCM/year). All the sub-basins show increase in groundwater level (mbgl) except the Kali Sindh, which shows decreasing trend (Q=-0.07 m/year). The extreme severe groundwater drought were estimated using Standard Groundwater Level Index (SGWLI), of the values for the Ram Ganga Confluence (SGWLI=2.44;2005), Upper stream of Gomti (SGWLI=2.06;2014), Ghaghra (SGWLI=2.22;2005), Ram Ganga (SGWLI=2.28;2005), Yamuna Lower (SGWLI=2.13;2007), Kali Sindh (SGWLI=2.30,2.67;2002,2003), Chambal Upper (SGWLI=2.30,2.20;2001,2003), Son (SGWLI=2.02;2010), Gandak (SGWLI=2.37;2010),Kosi (SGWLI=2.08;2012), Damodar (SGWLI=2.72;2010), and Bhagirathi (SGWLI=2.06;2014) were obtained for a period of 1996 to 2016 using a total of 62,050 observed well data.The obtained in-situ point data are converted into the surface raster using geostatistical technique. Our results show declining trend in the water budget of all the 19 sub-basin of the Ganga basin, and also the groundwater drought in several parts. Policy makers will benefit from our findings as they can use them to further UN Sustainable Development Goals such as ending poverty (SDG-1), hunger eradication (SDG-2), clean water & sanitation (SDG-6), socioeconomic development (SDG-8) and climate action (SDG-13) all of which must be accomplished before 2030.