In the Economic Survey 2020-2021, agriculture in GDP accounted for 19.9%, an increase from 17.8% in 2019-2020. The last time the industry provided such figures was in 2003-2004. Despite the current political climate surrounding India’s agricultural sector, it has witnessed a hit in the arm through a myriad of incentives this year. Like the allocation of 4000 crore to Pradhan Mantri Krishi Sinchayee Yojana to provide access to irrigated water to farmers. It’s all part of the government’s ambitious goal of doubling farm income by 2022.
At the same time, there is an urgent need to face the demand for increased agricultural production to feed a growing population. Agricultural production must be increased from 60 to 100% by 2050. The answer is not aggressive agriculture, but sustainable practices while combating climate change, depletion of natural resources, increased erosion, etc. .
To meet future food demand, data scientists rely on agribusiness to fuel innovation. Today, precision agriculture harnesses the power of artificial intelligence (AI). IoT, satellite imagery, drones, Web-GIS frameworks, big data, cloud and machine learning are expected to improve global agricultural productivity in the near future.
According to Indian Fertilizer Journal, precision agriculture is fundamentally the “right input” at the “right time” in the “right amount” in the “right place” and in the “right way” to improve productivity, conserve natural resources and avoid environmental tribulations. or social.
But to achieve these desired results, enormous amounts of data collection are required. Environmental data, thanks to technological intervention, has already fueled better agricultural techniques in developed countries.
However, it’s no secret that environmental data has crept in and is making positive changes in Indian agriculture.
Sensors and analytical tools can increase crop yields. To do this, environmental data is collected in geospatial format to measure quantifiable variables such as weather, soil moisture, volumetric soil temperature, fertilizer rates, water runoff, movement of soil. agrochemicals and rain.
Often, precision farming requires the use of non-destructive measurements such as remote sensing with geographic information systems (GIS) and global positioning systems (GPS). This rapidly expanding technology enables landowners to optimize production and minimize risk by combining satellite data and ground sensors that work in a network.
Companies like Tata Kisan Kendra (TKK) and Fasal are already implementing these technologies in India.
Data to be kept
Precision conservation, a subset of precision agriculture, is primarily limited to the conservation of soil and water in agricultural and natural ecosystems. Space technologies and procedures help create conservation management practices in natural and agricultural systems.
Again, global positioning systems, remote sensing and geographic information systems are integrated to obtain information that can be used for efficient use of land and water. For example, the Indian Maize Research Institute undertook the “Development of Precision Conservation Agriculture Practices in a Grain-Based System in the Indo-Gangetic Plains”. After two years, in 2020, project managers uncovered notable achievements, including water savings of around 82% in corn-wheat cropping systems compared to rice-wheat cropping systems.
Nutrient management practices based on soil testing have long helped improve food grain production. However, the efficiency of nutrient use has encountered obstacles. As a result, scientists and researchers have turned to improving crops instead of working the soil.
Reactive nitrogen losses will have a significant impact on the environment through nitrous oxide emissions, ammonia emissions, nitrate leaching losses and off-site transport of surface nitrogen losses. Using a GreenSeeker optical sensor, researchers from the Department of Soil Sciences at the Agricultural University of Punjab conducted seven field experiments in 2004-2006 to observe the sensor’s documentation in season and wheat production , as well as the application of nitrogen fertilizers. They observed higher yields and more efficient use of the fertilizer.
It is evident that environmental data, along with the application of analytics and AI, will certainly improve the current management of siled data.
Environmental data can help influence a paradigm shift to digitally transform agriculture using real-time dashboards that also monitor crops, water needs, fertilizer efficiency, market and conditions. economic. Environmental data thus paves the way for the modernization of agriculture. Of course, the technologies are there, but its large-scale implementation remains a dream to come true.
The writer is CTO and co-founder of Ambee