Participatory hydro-geological monitoring to enable decision making for smart and sensitive agriculture

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Participatory hydro-geological monitoring to enable decision making for smart and sensitive agriculture

India is the largest extractor of groundwater, and about 90% of which is used for irrigation. Changing rainfall patterns due to climate change has reduced dependency on rain-fed irrigation; while the efficiency of canal systems is such that the tail-ender villages do not receive adequate water. Over the years, the dependability on groundwater as a source for irrigation has increased, but excess withdrawal has also resulted in 17% of the assessment units (1186 out of 6881) declared as ‘overexploited’ by the Central Ground Water Board in 2017. Along with overexploitation, issues of groundwater contamination and increased salinity (100 assessment units are saline) affects the water quality, hence poses a threat. Areas near the coast face issues of sea water intrusion due to excess groundwater withdrawal.

With changing rainfall patterns and depleting groundwater resource, there is a need to carefully use water and land as a resource in agriculture. Smart agriculture helps in making decisions that result in maximising crop productivity with minimum resource exploitation and degradation. This involves understanding the local agro-ecological zone, geomorphology, subsurface conditions, while also regular monitoring of environmental conditions like weather, water quality and availability and soil health. When this is overlaid with socio-economic settings, local agricultural practices and market demands, an overall picture of the local agricultural scenario is developed. An integrated way can be then developed to incorporate use of technology in traditional agricultural practices.

Our partner organisation, Arid Communities and Technologies (ACT) works in Kachchh district and Khambhaliya taluka of Devbhumi Dwarka district. Both project areas are in the coastal regions but fall in different hydro-geological zones. Their focus is on groundwater management through participatory approach involving beneficiary communities in various stages of the project. Groundwater management involves supply side management which includes creating recharge structures, storage mechanisms; and demand side management which involves interventions at farm level such as optimising water use, soil treatment, cropping pattern. Data collection, management, analysis and dissemination is a part of demand side management for groundwater conservation. Decisions based on data analysis will help in resource conservation and improving its quality. 

Smart agriculture – Project vision:

ACT has collected aquifer data and monitored their projects based on that data for some years now. WIN Foundation and ACT have now also introduced technological interventions for data collection, partnering with start-ups who have provided data related equipment. Our project on data based decision making at farm level intends to incorporate local geographic, climatic, hydro-geological, water and soil studies for effective decision making in agriculture and irrigation practices for the farmers. Such multi layered study will be done by creating an integrated system of data collection, storage and analysis. The output of the continuous data collection and monitoring will be in the form of an advisory given to farmers, helping them make decisions for cropping pattern, water saving techniques and soil health improvement.

Such a system will focus on continuous data collection over a long period of time, to develop a data repository which will help to make decisions on farms.  Data collection at localized scale will help to get a clear picture of field conditions; and such collection done at multitude of field points will enable to gather the regional scenario. And so the system will need data collection points at various scales, covering clusters of farms and spreading to villages, clusters of villages, talukas, districts and so on. Decision making based on this system is a crucial tool for demand side water management. This could help reduce dependency on interventions designed only to augment water supply.

Project objectives:

  • To develop a system of data collection, storage, monitoring and analysis for parameters of weather, topography, aquifer, water availability, water and soil quality, water used for irrigation.
  • To use the data analysis for decision making for supply and demand side interventions such as cropping pattern, irrigation schedule, sowing and harvesting schedule, village water budgeting, and developing village water security plans.
  • To sensitise and train farmers for using technology and data guided decision making and incorporate it with their traditional agriculture practices.

Project framework:

To accomplish the project objectives, a framework is created that structures the overall vision of the project and guides its various components. The framework uses the following model, in which the input and process parameters are monitored as it affects the crop health and yield, which form a part of the output parameters. The monitored parameters are those that affect agriculture from atmosphere — surface — subsurface areas.

Figure 1: Model on which project framework is based

Using this broader framework, a demonstration unit is set up in the project villages of ACT’s current PGWM work in Kachchh. This demonstration unit consists of data collection instruments, equipment, tools and a defined methodology. The experience and learning from this demonstration unit will be used to implement such units at other locations and expand the data collection and monitoring system. These demonstration units should incorporate qualitative as well as quantitative data collection methods.

As seen in the diagram below, we are monitoring parameters affecting agriculture from the atmosphere — surface — subsurface areas, such as weather data, soil and subsurface data such as aquifer information, groundwater level, water and soil quality.

Expected outcome:

The analysis from the data collected would be used to create and share advisory among farmers, suggesting agricultural practices. The eventual goal would be to equip farmers to become self-sufficient, such that they interpret the data and make informed farm level decisions on their own. The process of data collection is participatory in nature, in which the farmer is involved in measuring certain data parameters, receiving analysed data and advisory. The process intends to familiarise farmers to data-guided decision making in farming, such that they are trained in scientific implementation in their current practices. Farmers as the end users should be able to reap the benefits of such data-guided systems in agriculture.

Natural resource conservation is closely linked with the local livelihood of farmers, cattle rearers and other allied occupations. And so, sustainability of soil and water is crucial given the uncertainty of climate change, as it directly impacts the rural livelihood. Through the envisioned smart agriculture system, water usage in irrigation could be optimized directly helping in water conservation, maintaining soil health and possibly crop health and yield.

Demonstration unit

Within the project villages of Arid Communities and Technologies and with the selected group of K-MARC farmers, a demonstration unit is set up which is local and decentralized in nature. The analysis from the data collected through this demonstration set-up will enable farmers to make informed decisions. This will be a step towards maximising resource efficiency and reducing its degradation. The demonstration unit monitors the following parameters:

Weather: To monitor weather parameters such as rainfall, humidity, temperature, UV index unity, wind direction and wind speed at local level, automatic weather station (AWS) is installed at two project villages. The collected data is directly saved in the cloud and can be accessed through a website login. An advisory system that gives weather forecast could be set up that informs farmers about rainfall forecast that helps in scheduling of irrigation, and temperature forecast helps to decide when sowing is to be done. We have partnered with SoilSens for installing automatic weather station and soil moisture meters in our field.

Water quality: A portable water quality testing kit to test parameters like Arsenic, Flouride, Nitrate, Total Iron, pH, EC and TDS is used. Its portability allows for testing water quality parameters at various geographic locations. It helps in getting the test results in a short duration of time and at a cheap cost. We have partnered with Foundation for Environmental Monitoring  for soil and water quality testing kits.

Soil quality: A portable soil quality testing kit to test parameters like Nitrogen, Phosphorous, Potassium and pH is used. Its portability allows for testing soil quality parameters at various geographic locations. It helps in getting the test results in a short duration of time and at a cheap cost.

Soil moisture: To measure soil moisture, a fixed moisture meter is installed in one project village, while 6 portable moisture meters are used in the project villages. The portable soil moisture meters allow for monitoring moisture levels at various points within the field. Based on soil moisture values, one can determine if there is adequate moisture content for crops, or if there is need for irrigation. It can also help in irrigating only when needed and avoid excess watering – which could affect crop health.

Groundwater level: An automatic water level sensor is installed in two neighboring wells, one is in use and the other is defunct. The defunct well is connected to a recharge structure. The changes in water level are measured through these sensors and the collected data is uploaded on a cloud server. These water level sensors help in understanding the effects of pumping as well recharge on groundwater table. When combined with the known aquifer properties, an understanding of radius of influence for these wells could be understood, as well as an estimate of discharge rates can be made. We have partnered with Connected Farms for digital groundwater level sensors.

In addition to the digital groundwater level measuring, our Bhujal Jankars (para-hydrogeologists) manually measure groundwater level and TDS for more than 200 monitoring wells every two months. 

Discharge rates: To measure discharge rates, 12 water flow meters are installed in three project villages; of which 10 are installed in the K-MARC farmers’ fields. When coupled with total pumping hours during a crop cycle, an estimate of volume of water used for irrigation can be made. This is an important tool in performing crop water budgeting and understanding total water used for irrigation for one crop cycle.

Model of collaboration

For such a demonstration unit to function smoothly, collaboration between stakeholders is needed, from financing, execution, continuous monitoring to upkeep and maintenance of the equipment. 

Economic inputs: The current mode of financing the demonstration unit is through project funding. The success and learning from such a unit can help develop other economic models. In such models the gram panchayat, village water committees or farmers’ groups can contribute either for installation or operation costs.

Capacity building: The current demonstration relies on training Bhujal Jankars for collecting data and carrying out field tests. The analysis of the field data then helps in making decisions from the demand and supply side management; also involving knowledge experts from the agriculture domain. For further extensive data collection, involving schools and its students for data collection could be planned, which would also help in students’ practical learning. A method of data validation will have to be incorporated.

We are also collaborating with academic institutes such as agriculture universities and Krishi Vigyan Kendra to help us in data analysis and creating advisory. This is an important link to connect data to farmers through the form of advisory dissemination. 

The demonstration unit will become more robust through the year, with different seasons and crop cycle data collection. This will help develop a data repository to give informed decisions and informative advisory. And learning from the unit will help replicate and scale up such models at other geographic locations.


AWS – Automatic Weather Station

ACTArid Communities and Technologies

pHPotential of Hydrogen

TDSTotal dissolved Solids

EC – Electrical Conductivity

K-MARC – Kankavati – Managed Aquifer Recharge through Community

UV – Ultra Violet

PGWM – Participatory Groundwater Management

We are keen to hear about your views or experiences in implementing agriculture data systems through the comment box. For more information, do reach out to us at


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