9.8 Billion! – according to a report by the United Nations, that’s the projected world population by 2050. To sustainably feed this population, the Food and Agriculture Organization (FAO) suggests that we need to raise the overall food production by 70% between 2005 and 2050. This means that developing countries need to produce double the amount of food.
“Civilization, as it is known today, could not have evolved, nor can it survive, without an adequate food supply.” – Norman Ernest Borlaug, “Father of the Green Revolution”
Our best approach forward is to incorporate modern technologies such as AI, Data Analytics, Machine Learning, Remote Sensing, and Drones to increase the efficiency and sustainability of agriculture. In short, pave our path towards digital agriculture.
What is digital agriculture?
Digital agriculture is defined as using Information and Communication Technologies (ICT) and the data ecosystem as an enabler to support the development and delivery of timely, targeted information to make farming profitable and sustainable. For example, farmers in Israel use sensors and data analytics to integrate agricultural data from multiple sources (weather stations, in-field sensors, and satellite images) onto their mobile applications. This has helped the farmers increase farm yield, reduce waste and make timely decisions. Such case studies have resulted in Israel being touted as the torchbearer in digital farming. Now that we understand what digital agriculture is, let’s try and understand how we can drive sustainable agriculture with digitalization.
Driving sustainable agriculture with digitalization
1- Real-time decision-making: Technology can be easily incorporated to enhance the efficiency of the agri-input supply chain. This enables rapid acquisition of the agri-input such as seeds, fertilizers, machinery, and swift decision making. Data for Decisions has successfully transformed businesses via its cutting-edge technological solutions. The agri-input companies are able to streamline operations, gain sales and inventory visibility, identify counterfeits and accelerate business growth by leveraging actionable data insights for real-time decision making.
2. Data warehousing: Handling data from heterogeneous sources is time-consuming and leads to slower decision-making. Businesses suffer from slower decision-making due to inconsistent data management and a lack of insight into the data. Data for Decisions helps businesses extract, transport, and load (ETL) data from multiple heterogeneous sources. The data is then arranged consistently and is further analyzed to derive vital, actionable insights for future decision-making.
3. Business intelligence: An example of digital farming is precision agriculture. Precision Agriculture is a data-driven approach to farming that helps increase the farm yield and overall productivity. This, in turn, leads to an increase in farm profitability. This is possible because data analytics suggest the need for agri-input such as water, fertilizers, and pesticides in optimum quantities. Cloud-enabled business intelligence (BI) tools ingest, analyze and visualize data to generate customized reports. The reports are presented in an interactive and easy-to-read dashboard. Data provides such tailored solutions for decisions which helps make intelligent decisions promptly.
4. Supply chain management: The agri-input supply chain management is the backbone of the agriculture value chain. Digital agriculture requires efficient supply chain management. Efficient supply chain management is possible when procurement and purchase order management is tracked and performed digitally. This would lead to rapid acquisition and implementation decisions. Technological solutions can also help identify the dead stock at each stakeholder level in the supply chain and provide clear insights. Real-time visibility of inventory and materials across the supply chain is crucial for digital farming.
A case in point is the Agrica Platform developed by Data for Decisions. It helps businesses harness technology to predict outcomes, prevent failures in real-time, and tap into the data to generate powerful insights.
Source: Data for Decisions
Conclusion
According to the Sustainable Development Goals (SDG) announced by the United Nations (UN), Goal 2 of SDG targets to end hunger for all people by 2030. It also envisions doubling the agricultural productivity and income of small-scale farmers. Goal 9 of the SDG aims to facilitate infrastructure development through enhanced financial, technological, and technical support. Such goals and agendas can be achieved by ushering technological solutions into the present agricultural and farming practices.
The responsibility of feeding the future world population lies on the shoulders of the present generation. Digital farming and digital agriculture are the answers to fulfilling that responsibility sustainably.
