Agriculture is facing enormous challenges as a consequence of the negative impacts of climate change. Climate change is reducing crop yields and the nutritional quality of major crops. Knowledge of climate variability is vital to minimize losses and maximize production in agriculture. Medium to long-term weather predictions is helpful in the planning and operation of agricultural activities. Several data-based models and decision support techniques are offering comprehensive solutions to understand these complex issues. The field of big data has become an advanced frontier of innovation and opportunities. Emerging big data analytics and climate change science together can accelerate the innovation of Climate-Smart Agriculture (CSA) and agricultural research. Of the entire big data industry, agriculture accounts for approximately 5% of the market share. The applications of big data in farming are not limited to just increasing crop production but also improving the effectiveness of the entire supply chain.
The agriculture industry plays a critical role in curbing the greenhouse gas emissions (GHG) contributed by the agriculture sector. Researchers apply big data analytics to agricultural and weather records to understand how climate variation impacts crop yields. Companies are racing to develop digital platforms that enable them to drive the decisions through data and analytics and allow farmers to make decisions based on multiple layers of farm data. For example, Bayer collects the data with its Climate FieldView platform and creates value for farmers through better-quality recommendations and advice for the products planted in their fields. Globally, the major players in big data in CSA include John Deere, AgJunction, AGCO Corporation, Raven Industries, AG Leader Technology, The Climate Corporation, and Precision Planting. Emerging markets like India can benefit from advanced farming technologies to mitigate climate change and protect environmental resources. Agtech start-ups in India such as CropIn, Fasal, Satsure, and AgriBolo are using machine learning and other computing techniques to help farmers fight climate change by offering innovative solutions. Industry leaders have mastered collaboration across large teams and promote shared responsibility for successfully delivering and executing recommended solutions.
Adopting new and modern tools and techniques by farmers is essential to balance food demand and supply concerns. Farmers are eyeing different ways to improve profitability and efficiency on the one hand and seeking ways to reduce their costs on the other hand to gain better prices for their products. Therefore, they must make better and more ideal decisions, specifically to moderate the influence of the weather and its impact. Though farmers have concerns about data ownership, they also see how much companies invest in Big Data and want to make sure that they reap the profits from big data. This change of thinking may pave the way for new business models that allow reaping the benefits from data. Conversely, most research in this area has evolved around commercial agricultural production in developed countries with relatively limited attention to big data-based solutions focused on smallholder farms in developing countries. The advent of internet-enabled mobile phones, the drop in the price of sensors, and the new network technologies are bringing big data into the small farmers’ territory.
However, the adoption of big data science has its own set of challenges. There is need to develop better satellite imagery, more accurate sensors, faster data storage, better software and hardware, and more innovative intelligent systems to overcome current technology challenges. A lot of big data analytic techniques such as predictive analysis, machine learning, clustering, to name a few, have to be thoroughly understood. The potential of data generated on farms remains unexploited because of the fragmented and uncertain policy environment governing the use of agricultural data. Agricultural policymakers can improve communication on how existing regulatory frameworks empower farmers to manage data collected on and about their farms and build confidence in using digital solutions in the sector. This could help reduce ambiguity, avoid needless uncertainty on cutting-edge data-driven solutions, and let all stakeholders leverage the potential of farming data for growth and innovation.
Despite these challenges, data science and big data present an incredible opportunity by providing novel methods, data, evaluation challenges, and vital information. Climate Science and Climate data analysis have helped data scientists understand the past and present environmental and climate conditions to establish trustworthy climate models. To sum up, big data provides predictive insights and increases the precision and accuracy of crop yields and thus allows to take real-time decisions and innovate game-changing business models.
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