Low 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.@en

Soil volumetric water content (VWC) is a vital parameter to understand several ecohydrological and environmental processes. Its cost-effective measurement can potentially drive various technological tools to promote data-driven sustainable agriculture through supplemental irrigation solutions, the lack of which has contributed to severe agricultural distress, particularly for smallholder farmers. The cost of commercially available VWC sensors varies over four orders of magnitude. A laboratory study characterizing and testing sensors from this wide range of cost categories, which is a prerequisite to explore their applicability for irrigation management, has not been conducted. Within this context, two low-cost capacitive sensors—SMEC300 and SM100—manufactured by Spectrum Technologies Inc. (Aurora, IL, USA), and two very low-cost resistive sensors—the Soil Hygrometer Detection Module Soil Moisture Sensor (YL100) by Electronicfans and the Generic Soil Moisture Sensor Module (YL69) by KitsGuru—were tested for performance in laboratory conditions. Each sensor was calibrated in different repacked soils, and tested to evaluate accuracy, precision and sensitivity to variations in temperature and salinity. The capacitive sensors were additionally tested for their performance in liquids of known dielectric constants, and a comparative analysis of the calibration equations developed in-house and provided by the manufacturer was carried out. The value for money of the sensors is reflected in their precision performance, i.e., the precision performance largely follows sensor costs. The other aspects of sensor performance do not necessarily follow sensor costs. The low-cost capacitive sensors were more accurate than manufacturer specifications, and could match the performance of the secondary standard sensor, after soil specific calibration. SMEC300 is accurate (MAE, RMSE, and RAE of 2.12%, 2.88% and 0.28 respectively), precise, and performed well considering its price as well as multi-purpose sensing capabilities. The less-expensive SM100 sensor had a better accuracy (MAE, RMSE, and RAE of 1.67%, 2.36% and 0.21 respectively) but poorer precision than the SMEC300. However, it was established as a robust, field ready, low-cost sensor due to its more consistent performance in soils (particularly the field soil) and superior performance in fluids. Both the capacitive sensors responded reasonably to variations in temperature and salinity conditions. Though the resistive sensors were less accurate and precise compared to the capacitive sensors, they performed well considering their cost category. The YL100 was more accurate (MAE, RMSE, and RAE of 3.51%, 5.21% and 0.37 respectively) than YL69 (MAE, RMSE, and RAE of 4.13%, 5.54%, and 0.41, respectively). However, YL69 outperformed YL100 in terms of precision, and response to temperature and salinity variations, to emerge as a more robust resistive sensor. These very low-cost sensors may be used in combination with more accurate sensors to better characterize the spatiotemporal variability of field scale soil moisture. The laboratory characterization conducted in this study is a prerequisite to estimate the effect of low-and very low-cost sensor measurements on the efficiency of soil moisture based irrigation scheduling systems.@en

Optimal, realtime and low-cost farm monitoring is a prerequisite to achieving the overarching goal of understanding small holder socio-hydrology and inspiring data-based management decisions to improve agricultural water use efficiency in developing countries. An experiment was conducted to comparatively analyse the ability of scientific grade and low-cost instruments in representing plot-scale soil water status through a cropping season. A second objective was to select a suitable methodology for interpolating the continuous but scarce measurements to ultimately generate representative plot-scale maps. The experimental plot corresponded to the size of a typical smallholding in the food producing Ganga floodplains (Kanpur, India) during the wheat cropping season. Two levels of cost cutting were implemented and tested against the scientific grade instruments procured from Germany. The first level of cost cutting was implemented using relatively cheaper, but scientific grade sensors manufactured in the USA with indigenously developed low-cost data loggers and transmitters for realtime communication. The second level of cost cutting was implemented using off-the-shelf extremely low-cost sensors and the indigenous data logger and/or transmitters. A comparative assessment was made between soil water representation by the high-cost, medium-cost and low-cost sensor setup. An analysis of the spatial dependence of the measurements was conducted to realize an effective sensor spacing. Different interpolation techniques were comparatively analysed to identify a suitable method to estimate plot-scale soil water properties, for each level of cost-cutting, to establish an effective trade-off between accurate representation of soil water status and cost. @en