How to improve farming decision support via recommendations
In the age of digitalized farming machinery, farming robots and artificial intelligence, companies in the agribusiness industry are collecting more and more data along the farming production process directly from fields and farms. On the other hand, they often lack standard solutions to efficiently use that data to optimize farming processes or provide better farming services.
With SAP Intelligent Agriculture customers have an enterprise grade tool at hand to streamline and standardize that data.
A new functionality called “Recommendations”, which was released recently provides a framework that helps end-users to create recommendations in SAP Intelligent Agriculture based on their assessment of the situation on the farm. These recommendations are aimed at the farm manager responsible for a farm, who can review and accept, alter or even reject the farming recommendations. In case of a rejection, the provider of the recommendation would receive a note from the farm manager containing the reason why the recommendation wasn’t accepted. Accepted recommendations will automatically turn into scheduled tasks that can be transferred to resources for execution.
Combined with the historic farm data that companies can store in SAP Intelligent Agriculture, Recommendations provide a fundamental feature for standardization and optimization of crop growing patterns.
With that SAP Intelligent Agriculture is now providing the next step towards a full end-to-end digital decision support process for agribusiness enterprises and opens up additional usage scenarios for farming service providers.
This functionality is available via API and UI for the maximum convenience level of our customers.
Recommendations can be used manyfold
SAP Intelligent Agriculture is an open platform that allows to use the functionality for enterprise farming companies that are growing their own crops as a direct decision support for their farmers within the growing season. Companies that are working together with contracted farmers in a consortium or as a consumer goods producer can leverage that functionality to support the contracted farmers with decision support on farming activities.
Companies that are providing farming services as a business model, consulting and executing farming tasks on behalf of their clients will also benefit from that functionality. Based on data collected on the operations of the fields and farms, those companies are now equipped to plan, create and maintain recommendations that can be handed over to farmers.
Agribusiness companies are working together with a variety of external service providers offering farming decision support solutions. The new functionality also helps to integrate those providers in a standardized way. A provider for intelligent fungicide management for example is now able to integrate recommendations about when, where and how to spray fungicide in an optimal way. These recommendations, once approved by the farmer, can be automatically transferred to the plan as farming tasks.
How to create a recommendation in the APP?
Let’s get our hands dirty and create a recommendation in the system. In our scenario, Katharina Agronomist, working for a consortium of farms, wants to assist the farm managers in optimizing their yield for the running growing season. Katharina was monitoring the soil conditions and the predicted yield for fields of the grain farm in the last couple of weeks and analyzed that it is necessary to schedule a fertilization program. She logs into SAP Intelligent Agriculture and starts creating a recommendation for the fields of the grain farm.
Step 1: Katharina logs into her SAP Intelligent Agriculture tenant as Agronomist and searches for the app tile with the title “Recommendations”. Once she found the tile, she clicks on it to enter.
Step 2: She sees a list of recommendations. Also, a button to create a new one. She clicks that button.
Hint: Alternatively, Katharina can access a recommendation from the list and use the “copy” function to create a deep copy of an existing recommendation. This is helpful to provide multiple recommendations at once.
Step 3: A new screen will open, and a new recommendation object is created in draft mode.
Step 4: Katharina enters all the necessary information.
She starts with the name of the recommendation, adds the farm, the date or period the recommendation is valid for. and clicks on “add recommended task”. Then she adds name, task type and task priority. In our case, she is also entering the field, the task is valid for and specifies the resources and recommended materials for the fertilization. After that, she confirms by clicking “apply”. With that, the newly created, recommended Task is added to the list of recommended tasks.
Step 5: The next step for Katharina is to finalize her recommendation by clicking “create”.
This will create the recommendation and afterwards switches the focus to an overview screen that is shown to the farm manager the recommendation is addressed to. He can decide to edit, approve, or reject it.
Approving the recommendation will create a fertilization task in SAP Intelligent Agriculture’s task management that can be viewed in the task scheduling or task list app.
Rejecting requires a reason that can be accessed and reviewed by the agronomist.
Benefits of Farming Recommendations in a nutshell
- Create personalized recommendations that help optimize farming processes and maximize field output
- Easily evaluate recommendations and approve, reject, or modify the recommended tasks
- Integrate recommendations from external systems through APIs
What is SAP Intelligent Agriculture all about?
SAP Intelligent Agriculture can help agribusinesses that run their own farm operations to increase their profitability per farmed area and support their efforts towards achieving sustainable farming practices. Also, the solution can be used to enable data-driven farming services and products that are marketed to farming business partners which generate new business and increase adoption and retention of new farmer collaboration channels.
Both are key priorities for Agribusinesses around the globe. Profitability in many farming operations is a key challenge and optimizing yields, quality of crop produces as well as optimizing use of crop inputs such as fertilizer, crop protection can have large effects. Many factors come into play on how to achieve this and farming operations differ significantly across the globe: different farm sizes, farming practices, different crops, level of mechanization and standardization etc. Still across all these differences we hear consensus that yields, and cost need to be improved by leveraging new types of relevant data.
