Data-Driven Decisions: The Future of Precision Farming

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The industrialisation and technological advancement in precision farming are hinged on data collation, interpretation, analysis, and utilisation for making informed data-driven decisions. These decisions become a prerequisite to taking actionable steps that can ameliorate or completely remove the farming challenges experienced as the pathway to the desired objectives is determined.

More specifically, precision farming entails the response to the overview of activities on the farm based on the data interpretation of specific operations and activities observed on the farm. There are quite several farming processes that can be captured on-farm. These activities must be quantified, interpreted, and transformed into digital data sets that application programs can utilise to produce precise interventions.

Agricultural data refers to data gathered and assessed from various sources within the operations of the farm. It includes information relevant to farming operations like crop yields, field locations, pesticide and fertilizer requirements, weather and rainfall patterns, soil conditions, etc. Those data ultimately provide the raw material that requires processing. The end product of data processing is information that charts the course for decisions to be taken.

Precision Farming Procedures

Precision farming entails the use of a management strategy that employs the details of site-specific processes including nutrient management, remote sensing, global information system, global positioning system, and variable rate application to precisely manage the production inputs. Managing crop production for example involves the right and appropriate use of inputs such as water, seeds, herbicides, pesticides, fertilizers, and many others to increase yield, quality, profit, and reduce waste efficiently and sustainably. The procedures promote optimisation operations i.e. operations that enhance getting the best or the most out of those applications. Precision farming intends to match agricultural inputs and practices as per crop and agro-climatic conditions to improve the accuracy of their applications. In the same way it is being utilised to monitor animals in real-time and improve their health, welfare, and production using the collection of electronic tools, sensors and devices.

According to an article by Just Agriculture, the procedures of precision farming entail assessing the variability, managing the variability and evaluating the variability. 

1. Accessing the Variability: Factors and the processes that regulate or control crop and animal performance must be accessed. Quantifying the variability of these factors and processes while determining when and where (time and space) different combinations are responsible is the main challenge for precision agriculture. These are also what the technologies are intensifying efforts to solve and implement.
2. Managing the Variability: When the variations are accessed, farmers can match agronomic inputs to known conditions that employ management recommendations. Those site-specific operations and variabilities utilises precision control equipment. For successful implementation, the concept of precision soil fertility management for example requires that the on-field variability that exists is accurately identified and reliably interpreted. That variability in turn influences crop yield, crop quality, and the environment. Therefore, inputs can be applied accurately. 
3. Evaluating the Variability: Evaluating the variability has significant input in ensuring that precise and economic applications are utilised for profitability while ensuring that it has a friendly and sustainable impact on the environment using the medium of technology.

Precision Agriculture Cycle

Some of the available technologies that enable us to understand the variability include Geographic Information System (GIS), Global Positioning System (GPS), Variable-Rate Technology (VRT), yield monitors, yield maps, remote sensing, and many others. The technologies help to give on-field specific agronomic and farming recommendations that can be used to manage the variability in crops and animals that makes the processes of precision farming viable.

Benefits of Data-Driven Decisions in the Future of Precision Farming

RIB indicates that Data Driven Decision Making (DDDM) is the process of using data to make informed and verified decisions to drive business growth. By using the right farming indices to address the limitations while projecting the expected objectives, farmers can make the best possible managerial decisions for productivity, sustainability, and profitability through the use of the precision farming technologies and tools.

Furthermore, data analytics is the portal that turns raw data into actionable insights, while optimising farm production management and resource allocation. It is the insights that help to make decisions. Some of the roles of Data-Driven Decision Making include:

1. Provides knowledge and innovation for farming operation: It plays critical role in providing knowledge and innovation in crop selection, pest management strategies, animal health and production indices and optimal resource allocation. Farmers can provide innovations in adjusting planting decisions based on weather forecasts, market demand predictions, and soil conditions while minimizing risks. Furthermore, it can help in measurable and specific solutions to areas of challenges on-field.
2. Continual growth of the farming enterprise: By collating data-driven insights from farming operations over time, farming operations can be predicted to promote growth, productivity, and increased efficiency. This further intensifies technologies through predictive models and machine learning to automate the farming processes and experience further.
3. Provides insights, understanding, and opportunities: Data-driven decisions help to provide the right insights, and the understanding of complex farming operations while also providing possibilities for many more opportunities for sustainable practices of farming possibilities in the near future. 
4. Reduces Production Costs and errors: Data-driven decisions help to reduce farming errors, misinterpretations, and production costs. The interventions to identify farming issues are specific and the solutions administered solve the root causes of the problems. 
5. Produces New Business Opportunities: Data-driven decisions can help produce new agribusiness opportunities in precision farming operations and scale precision farming activities. This is one of the major prospects of precision farming technologies, operations and management in the coming years.

Data-Driven Decision Making Process

Conclusion

Precision farming is evolving into more integration of technological solutions as the variabilities in farming operations are being increasingly understood. Data-Driven decisions as a consequence of data interpretation, and analysis will further play crucial roles in precision farming and subsequently shape the direction and utilisation of precision technologies in precision farming in the nearest future.