Spiros Mouzakitis

Giannis Tsapelas

Sotiris Pelekis

Simos Ntanopoulos

Dimitris Askounis

Sjoukje Osinga

Ioannis N Athanasiadis

Investigation of Common Big Data Analytics and Decision-Making Requirements Across Diverse Precision Agriculture and Livestock Farming Use Cases

Keywords: precision agriculture, livestock farming, big data analytics, decision-making, user requirements

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The recent technological advancements in Big Data, Artificial Intelligence (AI), High Performance Computing (HPC), Cloud Services and Internet of Things (IoT) have the potential to enable farmers to overcome long-standing challenges in exploiting the vast amount of data that can be collected, in order to increase efficiency and productivity while reducing the initial farm input costs [START_REF] Schimmelpfennig | Farm profits and adoption of precision agriculture[END_REF][START_REF] Schimmelpfennig | Sequential adoption and cost savings from precision agriculture[END_REF][START_REF] Janssen | Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology[END_REF]. Big data and AI enable novel precision agriculture opportunities that allow the performing of queries and analytics on a distributed and diverse set of collected data (from IoT devices, images, video, satellite data, etc.) that may lead to better and faster predictions and vital insights for farming decisions. IoT sensors on fields and crops [START_REF] Srbinovska | Environmental parameters monitoring in precision agriculture using wireless sensor networks[END_REF][START_REF] Mahlein | Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping[END_REF][START_REF] Vuran | Internet of underground things in precision agriculture: Architecture and technology aspects[END_REF] can provide a significant amount of information, critical for the decision-making process such as soil conditions, high-fidelity weather conditions [START_REF] Bendre | Big data in precision agriculture: Weather forecasting for future farming[END_REF][START_REF] Keswani | Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms[END_REF], fertilising requirements, water availability and pest infestations. In addition, aerial images captured by unmanned aerial vehicles [START_REF] Wolf | Unmanned Aerial Vehicles for Precision Agriculture in Orchard Crops[END_REF], or drones, which can patrol fields, can provide early warnings related to potential problems, such as diseases or deviations from expected growth rates; or offer indicators of crop ripeness and quality. Individual plants may also be monitored for nutrients and growth rates. Satellites [START_REF] Gevaert | Generation of spectraltemporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications[END_REF] can facilitate detection of relevant changes in field with captured satellite imagery, identification of crop threats such as nutrients deficiency or insect damage, while GPS units on tractors can help determine optimal usage of heavy equipment and precise management of field operations. At the same time, livestock production management exploits technology to quantitatively measure the behaviour, health and performance of animals, including real-time monitoring of reproduction, health and welfare of livestock and the corresponding environmental impact [START_REF] Wolfert | Big data in smart farming-a review[END_REF][START_REF] Van Der Heide | Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle[END_REF]. The data sources utilised in livestock management include amongst others on-line sound and video observations, feeding intake, drinking behaviour data, data from sensors on the animals and data from milking robots. There is an increasing literature of individual use-cases on precision agriculture and livestock farming applications, nevertheless the identification of common requirements and challenges across different use cases is currently missing.

The purpose of this paper is to present the investigation of common requirements and needs of users across a diverse set of precision agriculture and livestock farming use cases that was based on a series of interviews with experts and farmers.

Methods and Approach

Overall Approach

For the identification of the requirements a close collaboration with twelve agri-food industry stakeholders took place between January and June 2019 and consisted of three phases: a) Structured interviews with the stakeholders describing the current situation, the expectations for an ideal, future situation and the challenges to be addressed in between b) Using the initial current and future situations from the first phase, nine Usage Scenarios were created in a collaborative manner with data engineers, analysts and HPC experts c) A list of common user stories was distilled from all the usage scenarios and were ranked based on their foreseen business impact and technological complexity

The requirement investigation process took into consideration the user-varying dynamics in the precision agriculture and livestock farming scenarios, that is the different sectors, expectations, backgrounds and interests of users involved in different tasks such as data collection, data cleaning, modelling, decision making, and application of the outcome of the tasks for particular purposes.

Each user task is distinguished by several aspects: the data needed for this task, the skills required to perform the task, the timing of this task, and so on. People who assume the role of performing the task can be end users (e.g. farmers, farmer cooperatives), intermediaries (e.g. extension officers, veterinarians, seed providers), companies (e.g. planning institutions), data professionals (e.g. technical assistants, data analysts / scientists), and so on. Nonetheless, the user is not necessarily a person, the term could also describe a device (like a sensor or a satellite), an application program, an HPC cloud function, or any agent performing a task.

In order to avoid confusion around the term 'user', the usage of 'roles' was preferred in the analysis. A role is the particular function a user fulfils when carrying out a particular task, such as the role of data collection or the role of data cleaning. Each role has a body fulfilling that role. Several roles may be combined by the same body of fulfilment. The final user of the use case is not necessarily the same as the end user or stakeholder who benefits from the solution that the use case provides. A use case may end at the point where an intermediary is required to bring the outcome to a farmer in order to help the farmer solve a problem. The farmer himself/ herself may not be within the scope of the technological solution, but he is a stakeholder nevertheless. To avoid confusion on that, we use the term 'stakeholder' exclusively for the one who ultimately benefits from use case solutions developed in the context of the ICT solution but who is not necessarily part of the scope of the use case, and hence not necessarily himself/ herself a user of the technological solution.

Interviews

Initially, a set of individual online and physical one-to-one interviews were conducted between March and April 2019. During the interviews, workflow diagrams were identified and drawn together with the interviewees, based on their descriptions. When more than one situation could apply for "current situation" or "future situation", they were all included in the interview. Hence, each interview resulted to a set of workflow diagrams that consisted of at least two, but often more diagrams. Following, each diagram was discussed with respect to the roles involved in every step of the workflow. These roles were placed in the diagrams, labelled and described as accurately as possible at that time. Finally, the stakeholders for each use case were identified as well, with elaborated descriptions of how they can be distinguished from one another, and how they inform themselves and communicate within and outside their peer networks. This in-depth description of the stakeholders and the ways they operate guided the process of deriving the requirements that best match their needs and expectations.

User Scenarios Co-Design Process

Following the interviews, nine usage scenarios were co-designed through the collaboration of agrifood-industry stakeholders and technical staff (data engineers, analysts, HPC experts). The usage scenarios are descriptions of hypothetical real-world examples. They are essential in order to highlight the interactions of people and organisations with a system. They contain detailed references to the steps, events, and/or actions which occur during these interactions. More specifically, the scenarios in this document attempt to describe, exhaustively and on a high-level, all the possible ways the users will interact with the proposed technical solutions and accurately depict their critical business actions. Hence, the scenarios show how the various users wish to improve their workflow with available state-of-the-art solutions. Critical properties were specified by the information provided in the scenarios regarding e.g. the type of device the users will access the developed application from, locations where data can be stored, privacy and security constraints, existing solutions. Agrifood stakeholders outlined their business needs and challenges while the technical partners reviewed the scenarios and updated them with proposed state-of-the-art ICT solutions (infrastructure, data workflows, software tools and algorithms). In order for the reader to develop a better understanding of the concept, a summary of an indicative user scenario, as it was designed by the stakeholders of a fish farm, is provided in section 2.4.

An Example of a User Scenario

Datafish is an SME that develops precision livestock farming ICT solutions for aquaculture by deploying IoT sensors and AI. Datafish wants to develop a platform solution that will aid Cretafish, a fish-farming company in Crete, in monitoring fish behavior in fish tanks and optimizing the decision-making in fish diseases, fish feeding and environmental impacts of the fish farm. Cretafish is currently adjusting the amount of food to be thrown in the cages by observing the fish at the surface level during the feedings. Datafish will incorporate near real-time processing of aerial drone and satellite images as well as sensor collected data such as water temperature, salinity, maximum chlorophyll index (MCI) etc. Datafish will also include several components that support multiple data operations and visualisations, machine learning modeling capabilities and reporting services, in order to enable the generation of valuable information to be leveraged by Cretafish.

Data Operations and Simple Visualisations Component.

• Data Management. This module automatically receives relevant data available data to the platform (e.g. weather data). Additionally, it allows for an authorised Datafish employee to gain access to the datasets that are generated by sensors and cameras. She/ He can regularly clean and upload them to the solution's platform where they are securely stored in a private cloud. • Data Exploration. This module permits the exploration of each dataset e.g. aerial images for observing fish behaviour, temperature and O2 levels to monitor fish health. • Simple Visualisations. This module provides interactive graphs such as barcharts that combine generated feeding patterns with weather data in order to assess their correlation. Experiment/Analytics Environment. An authorised Datafish data analyst, uses this component to gain access to a development environment built to support her/ his favorite programming language -e.g. Python-as well as her/ his most often-used machine learning algorithms and datasets. She/ He writes the code in order to achieve:

• Fish feeding optimisation • Algae bloom prediction • Fish disease prediction Dashboards/Advanced Visualisations Component. The component includes a realtime dynamic heatmap of the fish tank and advanced graphs showing the amount of food, the recommended optimal feeding plan and an overview of adjustable fish parameters. It also provides historical versions of the heatmap for on-the-go comparisons, as well as a camera feed with zoom capabilities that will enable the inspection of individual and group fish behavior and will facilitate disease detection. Reporting Component. The component enables Datafish to automate the reporting process so that Cretafish receives daily updates that include parts of the Dashboards and a summary report on each cage. Cretafish can also view the dashboards in real-time when feeding is taking place so to optimise future decision-making. Alerts Component. Cretafish uses this component in order to receive disease warnings based on anomaly detection techniques and alerts relevant to the duration and amount of feeding. Model Template Component. After approximately six months of application, this system becomes a model template ready to be used by other fish farms through this component.

Requirements Elicitation and Ranking

A list of user stories was elicitated from the case-specific usage scenarios in the form of "As a <Role>", "I want to <user requirement>", "So that <benefit>" and grouped together based on the use cases. Thereafter the stakeholders were asked to rank the derived requirements based on: a) Business value: The business value was asked to be provided by the agrifood stakeholders, in a qualitative manner using a scale between 1-5 points, by estimating the importance and foreseen business impact of this requirement for the overall process. b) Technical Complexity: The Technical complexity was calculated by the technical partners through voting, on a scale for 1 to 5, with 1 meaning "least complex" and 5 "most complex" This methodology poses as an extension to an approach proposed by Lant [START_REF] Lant | How to Easily Prioritize Your Agile Stories[END_REF] which calculates a score for every user story, against two different attributes, that of business value and urgency. Through the methodology presented above and by qualitatively examining the requirements and the dependencies among some of them, we concluded on a final set of common requirements for Big Data analysis and decision-making across the nine different use cases of precision agriculture and livestock farming practice.

3

Interview Results per Use Case

Organic Soya Yield and Protein-Content Prediction

This use case involves the prediction of yield and protein-content maps based on satellite imagery and additional information concerning electromagnetic soil scans, drone images and sensory data. Time-series of satellite images are very indicative of the relative yield and protein content on the field, i.e. they can pinpoint the areas in which soybean grows better or worse. By knowing the absolute values of yield and protein content on the whole farm, these traits can be reverse-engineered in these specific areas of the field and derive the corresponding maps.

During the interview, the need for data fusion was recognised, as data will be acquired from heterogeneous sources (EC probes, satellites, drones) at different times. Although monitoring in one farm may not be computationally intensive, monitoring the soybean production at field-scale throughout Europe requires huge processing power and smart algorithms, optimised for parallel execution. The size of the dataset and the classification algorithm that has to be trained and executed, require efficient data management and strong processing power through HPC infrastructure and Big Data solutions.

Climate-Smart Predictive Models for Viticulture

The purpose of this use case is to support complex, highly non-linear, models for vine and grape growth with respect to the extreme number of variables (data types) that have been shown to affect the quality and quantity of the produced yields. Such crop models could estimate vine and grape growth and crop yield at larger scales, with spatial sources of information on soil, water, land use, and other factors. In this way, much larger predictions of yield could be achieved across regional scales.

During the interview it was clarified that this use case involves large matrix operations, which are memory-intensive computations. The limited amount of computer memory available in a non-HPC enabled deployment does not allow for an efficient parallelisation of the data processes entailed in the solution.

Climate Services for Organic Fruit Production

Integration and comparison of fruit bud development estimation models with temperature and air humidity forecasts and other ancillary data can be used for risk probability mapping in order to establish an early warning system that can help farms to minimise damage effects through protective methods for frost and hail. This use case focuses on climate predictors that are correlated with either frost or hail occurrences and then can be used for planning risk prevention operations.

During the interview the need for examining the integration of a frost and hail early warning system as a climate service into a decision support system for horticultural and fruit-tree farmers was presented. This service is based on exploring and analyzing the best probabilistic predictions of these extreme climate events for site-specific spatial scales. Observational and simulated climate variables need to be integrated with crop modelling approaches to estimate the probable risk. This requires exploration and comparison of different methods together with forecast quality assessment of the predictions, in which synchronous observed and predicted values are compared.

Autonomous Robotic Systems within Arable Arameworks

This case considers the provision of autonomous robotic systems within an arable farm. Dictated by the weather, farming tasks have often to be carried out within a short time window. Consequently, equipment has increased in size to complete the work rapidly. One alternative solution is for farmers to manage fleets of smaller, autonomous vehicles and carry out the tasks as required.

During the interview, the to-be situation focused on field robots obtaining sensory data for soil chemical analysis (regularly/monthly) and hyperspectral imaging (HSI) for determining soil and crop conditions. Such data can be used for real time object level plant identification, individual plant harvest readiness assessment (in near harvest periods) and plant-level automated harvesting (which is currently labour intensive). The challenge in this use case lies in the precise processing of sensory data not only for identifying plant, weed and arable land readiness for harvesting, but also for activating nearby actuators distributed across a number of vehicles.

Optimizing Computations for Crop Yield Forecasting

Crop yield monitoring can be used as a tool for agricultural monitoring (e.g. early warning & anomaly detection), index-based insurance (index estimates) and farmer advisory services to facilitate precision agriculture and timely identify in-field phenomics by helping to provide greater yields and contributing to better food security.

The interview process highlighted that current computation loads over a single server have been reduced to meet hardware limitations. Additionally, more detailed weather forecasts from ECWMF together with parcel-specific data, and data processed for Sentinel Satellite Imagery may allow to predict crop yields at parcel level. Hence, one of the main challenges is distributing the computational load over several computational resources and exploring the potential of machine learning algorithms to forecast yields at parcel level.

Pig Weighing Optimisation

This use case has three main goals: (1) To estimate the mean and standard deviation of the live weight of grower/finisher pigs in a pen based on video images; (2) To track the weight of individual pigs in a pen based on video images; (3) To incorporate the growth curve estimated by the Convolutional Neural Networks (CNNs) in previously developed models for early warning of diarrhea. Currently there is no video-based weight estimation available and an eye-balling estimate is used on common farms due to manual weighing on big farms being a laborious task.

The interview process revealed that the training of the above described CNNs with the already available big data is an inherently immensely computationally demanding task which cannot be handled by limited computational power and memory infrastructures. Moreover, image and video processing and analysis is a task that can highly benefit from HPCs. The sensors that are installed are generating large amounts of video and signal data and this will be an even bigger issue as additional sensors might be installed in the future.

Sustainable Pig Production

This use case focuses on improving pig health and welfare, works on fulfilling the potential of each pig through its life and on increasing the quality of the end-product for the market and the consumers. This requires the usage and data fusion of various data sources coming from multiple on-farm sensors and software systems, image analysis, management data and slaughterhouse records.

During the interview, the need for exploring techniques for data fusion was identified, particularly for data of different size and sampling frequencies. Additional needs include high-throughput processing of big data with multivariate algorithms and advanced machine learning, as deep learning, to automatically detect data anomalies. The outcomes can include warnings (alerts) of problems with health, welfare or productivity, development of longitudinal trends, and data on individual pigs and prediction of end-product quality.

Open Sea Fishing

The goal of this use case is to achieve higher sustainability of a fishing fleet, rebuild overfished stocks and prevent overfishing. First, the integration of data from the entire fleet's electronic logbooks is necessary and requires a series of improvements of the on-board database systems of commercial fishing vessels. Additionally, the collection, storage and processing of on-board sensor data is required, together with a visual-based processing of catch imagery deriving from RGB cameras for fish-selection purposes During the interview it was revealed that the preprocessing involved is computationally intensive and requiring a scalable infrastructure that offers parallel processing. Moreover, a multivariate analysis model that integrates all available sensor and price data as well as an artificial neural network to find optimal operational parameters for minimal costs is required and is highly computationally intensive.

Aquaculture Monitoring and Feeding Optimisation

The purpose of this use case is to investigate fish behaviour on a deeper level. To do this, methods like segmentation and region proposal, object tracking, video analysis and machine learning are used to analyse water movements from colour, to detect problems in nets and cages, and to determine fish positions. This information will be combined with other data such as weather information and sensor measurements (mainly related to oxygen and current speed) in order to develop an efficient feed management system that can help companies to make optimal use of the food, reduce costs and also reduce the impact on the environment.

The interview revealed that data processing needs to be performed within short time frame. Being able to process data fast, and extract insights are the big challenges of this use case, demanding high throughput, computational intensity and short turnaround times.

Derived Common Requirements from All Use Cases

For each use case presented above, a set of usage scenarios was created along with the corresponding set of user stories. Thereafter all user stories were analysed and classified in eight categories (data management, data storage, data exploration, data analysis, data process, visualisations, support, alerts). Common requirements across all use cases and user stories were identified and are presented in the following Table 1. Ability to create models in different programming language (R, python, Scala, Spark MLlib) depending on the expertise / preference of each user. Do not lock-in the use case in a specific programming language Data Analysis Be able to validate using reference historic data from previous projects to recalibrate or fine tune the system to each user's case in order to leverage existing data and assist the quick training of the models. Data Analysis Ability to enable semi-automatic generation of metadata, in order to save time from manual data input Data Analysis Support automated detection of anomalies in output from the simulation models that run (executed efficiently on HPC clusters) Data Analysis

Apply classification algorithms on both images and other input variables Data Analysis

Train & optimise Machine Learning and Deep Learning models and evaluate them using different parameters in a parallel way (tool examples: Horvod, Tensorflow) Data Process Support indications of the parts of the code that are memory or processing intensive and may require parallelisation, i.e. using HPC or Apache Spark functionality Data Process Process video & image data and extract features within short time windows (near real-time responses)

Data Process

Be able to manage the resources being used by the experiments [e.g. Workflow management dashboard (edit, pause, delete, view consumed sources)] in order to be aware and be able to manage resources used respectively to the workload of each task Data Process Make use of the best distributed frameworks, the best available software libraries for scientific computation (such as MKL or openBLAS) Data Process Smart distribution of data and parallelisation of tasks based on multiobjective optimisations (e.g. by origin / destination of the data, available resources) and best parameter setting for the productivity model (e.g. according to environmental, regional and weather conditions)

Data Process

Interpolate the available data with many (e.g. at least 20) different weather forecasts and satellite images in order to increase the robustness and practicality of predictions

Visualisations

Be able to create standard and custom visualisations over the data that users with basic ICT knowledge can easily interact with farmers/ fishers, etc.

Visualisations

Be able to combine different series of data, create custom visualisations, charts, maps and graphs of selected variables in order to gain intuition over data, models and events and other statistical observations Visualisations Support map-based visualisations based on HPC software that require real time data processing Visualisations Combine multiple time series in the same graph in order to visually compare and correlate multiple variables in time

Support

Have expert technicians install hardware (cameras and sensors) at the farm (or premises) to enrich the existing databases at minimal cost Alerts Ability to receive customisable real-time notifications and warnings in order to make timely decisions

Conclusions and Future Work

The present paper presented the interviews conducted in nine (9) diverse precision agriculture and livestock farming cases in order to identify common requirements and challenges in terms of data collection and management, Big Data, HPC infrastructure and decision making.

The interviews outlined and revealed a set of common user requirements and indicated the users' point of view concerning the necessity of a robust and scalable infrastructure that would enable better and faster predictions and data analytics taking advantage of the fusion of different and timely updated datasets. The users put emphasis on the importance of the low cost and the ease of installation and operation of such infrastructure and decision-making tools. A high development and maintenance cost could render the technological solutions inapplicable. The common requirements that derived from the interviews and user requirement analysis per use case can serve as basis for identifying functional and non-functional requirements of a technological solution of high re-usability, interoperability, adaptability and overall efficiency in terms of addressing common needs for precision agriculture and livestock farming. Future work involves the development of a blueprint architecture and design principles for technological solutions in precision agriculture and livestock farming, as well as the development of a prototype platform that combines HPC, Big Data, Cloud Computing (services) and IoT. Their main purpose will be to provide integrated and unmediated access to a vast number of large-scale datasets of diverse types, coming from a plethora of different sources, as well as to enable the actual generation of value and extraction of insights out of these data.

Table 1 .

 1 Common requirements from all use cases investigated

	Category	Requirement
	Data Management	Guarantee data, model & outputs integrity through the entire data value chain in order to develop a trustful relationship between stakeholders (e.g. farmer & buyer)
	Data	Be able to upload new datasets, with different schemas, in a common
	Management	data repository, from different sources
	Data Management	Support automated check-in of sensor-collected data
	Data Management	Daily transfer & access to daily updated data (e.g. parcel specific weather forecasts, satellite imagery data, logbooks, satellite VMS data etc.)
	Data	HPC infrastructure for near real-time image processing in order to al-
	Management	low near real-time notifications
	Data	Ability to share results and visualisations with other users to increase
	Management	collaboration opportunities and efficiency
	Data	Ability to store as template a whole project, or smaller workflows of it,
	Management	ready to be trained with new input datasets
		Access to parcel-specific soil data, crop parameters, crop calendars,
	Data	historical weather archives, regional statistics & historic yield data
	Management	(parcel-specific N/A but desired) in order to correlate them to conduct
		analysis and feed them to crop models
	Data	Expose data and results through APIs for increased interoperability
	Management	with other systems
	Data Storage	A private cloud storage is required with fine-grained access control
	Data Storage	Encryption of data on the private cloud storage

Acknowledgements

This work has been co-funded by the CYBELE project, a European Commission research program under H2020-825355. We are particularly grateful to the interviewees from the nine CYBELE case study demonstrators.


Gerald Schimak
email: gerald.schimak@ait.ac.at

Dražen Ignjatović
email: drazen.ignjatovic@ait.ac.at

Erik Vullings
email: erik.vullings@tno.nl

Maurice Sammels
email: sammels@xvrsim.com

Interoperability of solutions in a Crisis Management environment showcased in Trial-Austria

Keywords: Crisis Management, DRIVER+, Trial, earthquake, interoperability, innovative solutions, environmental hazards 1 Introduction / Trial / Scenario 1.1

 an EU-funded project launched in 2014 was setting up a technical infrastructure (so called Test-bed) that allows to interconnect solutions, so they can interact and exchange all crisis relevant information that commanders need, to make their decisions and plan their actions related to a specific crisis. To verify the DRIVER+ Test-bed as well as the DRIVER+ Trial Guidance Methodology [3] and furthermore, to overcome identified CM gaps [2], a series of Trials was setup. Trial-Austria was the fourth one to be executed. This Trial was especially challenging as it was held as a field exercise in parallel to a huge European Civil Protection Exercise (called IRONORE2019). The scenario to be dealt with was an earthquake scenario. The developed methodology, Test-bed as well as various solutions taking part in DRIVER+ are a perfect base and platform to deal with whatever hazard (e.g. chemical, physical, etc.) is endangering our environment or wellbeing.

Introduction

The scale and pace of crises pose enormous challenges to the Crisis Management (CM) sector, with new threats emerging all the time. An already complex field must also strive to integrate new technologies and methods, cope with a rapidly changing infrastructure, understand evolving risks, be effective across cultural, administrative and national boundaries and engage with populations to enhance their resilience. Innovation is therefore critical but will only be successful if it is relevant and accessible to practitioners and operators. Many crises involve interfacing diverse CM systems and solutions. Major crises can also frequently involve more than one country or region, which may have differing CM infrastructures and cultures. It is also highly likely that this will necessitate interfacing different systems and combining different solutions. CM innovation must therefore be capable of meeting these multifaceted challenges and delivering solutions that are modular, flexible adaptable and interoperable.

The DRIVER+ Project [START_REF]Project DRIVER+[END_REF] is dealing exactly with such challenges and within the project's run period (2014-2020) so-called Trials were launched, testing various technical and non-technical solutions.

Furthermore, DRIVER+ seeks to improve the way capability development and innovation management are tackled, by testing and evaluating solutions that address the operational needs of practitioners dealing with Crisis Management (CM).

Understanding the main problems that CM practitioners are currently facing is important to ensure that the project's results correspond to the practitioner's needs.

Thus, DRIVER+ drew up a list of 21 gaps [2] organized in five CM functional domains: decision support; information sharing and coordination; engaging the population; resource planning and logistics, as well as casualty management.

Four Trials (held in Poland, France, Netherlands and Austria) were set up and conducted during the project, which focused on these gaps, by identifying the differences between the current capability and the capability deemed necessary for an adequate performance of one or more crisis management tasks.

Trial Guidance Methodology

The Trial Guidance Methodology (TGM) [3,4] is designed for Crisis Management practitioners as it facilitates a robust assessment of innovative solutions. The TGM provides step-by-step guidelines on how to assess them in non-operational contexts (such as a Trial) through a structured approach. The methodology consists of three phases: preparation, execution and evaluation. The preparation phase results in a Trial design with multiple elements that are captured within a Trial Action Plan document, whose main outcome is meant to be applied and executed in the second phase. The Trial committee is responsible to ensure that all decisions taken in the first phase can be executed.

Each Trial consists of three elements: the tailoring of the Test-bed in accordance with the Trial design, the finalization and simulation of the identified scenario within the Test-bed, and the ability to carry out an assessment of the three DRIVER+ performance measurement dimensions (i.e. CM-, Trial-, solution dimension).

The execution phase ends with the running of the actual Trial, through the simulation of the pre-defined scenario, the deployment of potential innovative solutions and the collection of relevant data. In addition to the data collected during the Trial, additional feedback from external stakeholders (participating actively as Trial actors or passively as observers) is gathered after the main event.

During the third phase, the gathered data is processed in order to assess and analyse the real impact of the innovative solutions. This information is not only very useful for the CM practitioners but is also valuable for the solution providers concerned with further improvement of their solutions.

DRIVER+ has developed and issued a Trial Guidance Methodology Handbook providing an overview of what crisis managers would need to do in order to depict a specific operation and integrate new socio-technical solutions in their ways of working. The Handbook can be downloaded from [3]. It offers not only a guidance on what to do by whom and when, but also introduce appropriate tools and methods to conduct those tasks. It also provides information about the DRIVER+ Test-bed technical infrastructure.

Test-bed

The main purpose of the DRIVER+ technical infrastructure [START_REF]DRIVER+ Test-bed specification[END_REF][START_REF] Vullings | An interoperability Framework for Trials and Exercises[END_REF] Fig. 1 presents the flow of information and interactions between the actors on the one hand and the involved solutions and tools on the other hand.

The flow as indicated in the Fig. 1 is as following:

1. The Trial starts: storylines are activated, and the fictitious crisis evolves. 2. Simulators process storylines and additional operator actions. Simulator data is sent to the solutions. 3. Solutions are fed with simulator data, they share information, and request actions from the Simulators. 4. Observers create observations, which are shared and recorded in the Test-bed. 5. The Trial ends and all logs and observations are collected for evaluation.

The connected components as shown in Fig. 1 are:  SOLUTIONS: The solutions are assessed during the Trial. They can be connected to the Test-bed via CIS adapters such that they can send and receive data from other solutions and simulators.  Common Information Space (CIS): Set of KAFKA topics [START_REF] Kafka | [END_REF] to exchange data between solutions, to receive data from and send commands to simulators.  SIMULATORS: Provide a fictitious crisis during the Trial for participants and solutions, so solutions can be evaluated effectively in a realistic setting and such that participants feel immersed in the simulated crisis. They offer data and visualizations, such as 3D virtual reality views, flooding plots, fire progressions, panicking crowds and jammed traffic, simulated (social) media messages or a regional/national set of available resources.  Common Simulation Space (CSS): Set of KAFKA topics to exchange information between simulators, so they are synchronized and can act as one. Simulators send, via a gateway to the Common Information Space, data to solutions and receive instructions to be executed.  Trial Management Tool: Acts as composer and conductor, offering the Trial staff control over the Trial. During preparation, the staff can create storylines and acts, which represent possible evolutions of the simulated crisis. During the Trial itself, the staff can start and pause the Trial, its storylines and acts, thereby influencing the direction of the Trial and the challenges that the participants face.  Observer Support Tool: Runs on tablets and in browsers, to create observations quickly that are targeted at specific moments in time during the Trial.  After Action Review: Facilitates a detailed, data-based evaluation after the Trial.

Stores all messages and observations exchanged during the Trial execution, as well as screenshots from running applications, so it can be reviewed together.

The added value of the Test-bed is that it supports not only the interoperability of solutions but also enhances the quality and realism of trainings and exercises, supports in the evaluation of all actions played/executed during the Trial and is available as open source and free of charge.

To deploy, configure and run the Test-bed technical infrastructure anytime and anywhere, and to simplify connecting solutions and simulators, these extra components (see Fig. 2) are available to software developers and system administrators. A detailed description of the Test-bed can be found at [START_REF]DRIVER+ Test-bed design[END_REF].

Benefits of carrying out Trials

The objective of a Trial is to assess and evaluate solutions in a realistic CM environment by providing a technical infrastructure for them to connect to each other and to provide a possibility for improvements by getting feedback from practitioners using the systems, applications or apps. The DRIVER+ approach takes as a starting point the fact that there is a strong innovation momentum present in the Crisis Management community. At the same time, there is inertia to change, which can prevent this momentum from resulting in sustainable improvement. This points to the need for a better evidence base for Crisis Management capability investment decisions.

Innovation is critical but will only be successful if it is relevant and accessible to practitioners and operators. This is exactly what the Trial is aiming for, namely to test innovative solutions under simulated crisis conditions, by gradually adapting them to operational constraints, as well as creating acceptance among users through their active involvement and by providing evidence to decision makers that they are cost-effective.

Trial-Austria

In a nutshell: Trial-Austria focused on a severe earthquake and subsequent heavy rains simulated in the central area of Austria, causing extensive damage in the most affected area, the local region of Eisenerz (in Styria, Austria).

The main objective of Trial-Austria was to find solutions that overcome shortcomings and limitations [2] in the management and monitoring of spontaneous as well as affiliated volunteers on the crisis scene in terms of location, tasking, capabilities and duration of operations. It was also to highlight the ability to merge and synthesize disparate data sources and models in real time (e.g. visualization of resources, critical assets map, damaged objects/ infrastructure etc.) to support incident commander decision making, situation assessment and exchanging crisis-related information.

The Trial also focused on (non-technical) solutions for providing psychological first aid and support as well as interaction with population (e.g. foster communication capabilities, registration of affected people, provide safety information, etc.).

Trial-Austria was organized by AIT Austrian Institute of Technology and hosted by the Austrian Red Cross in the centre of Austria at Eisenerz/Münichtal from Thursday 12 th to Saturday 14 th September 2019. The Trial was conducted as a multi-day field exercise run in parallel to the large-scale European Civil Protection exercise called IRONORE2019 [9].

Solutions in the Trial

CrowdTasker

CrowdTasker is a solution (from AIT Austrian Institute of Technology) for citizen involvement and community interaction. It supports informing citizens, eliciting contributions to the common operational picture by pre-registered parties and integrating efforts of self-organization. This is achieved by issuing assignments and situational information to a selected group of citizens based on their location and skill set, as well as offering a chatbot interface for emergent groups to participate using their own organizational infrastructure (such as social media groups).

The objective of CrowdTasker is to improve informed decision-making of both crisis managers and citizens. It enables professionals to rapidly query information from users at relevant locations and to provide meaningful assignments to citizens during preparation, mitigation and response. CrowdTasker helps to include several forms of volunteering [START_REF] Middelhoff | Crowdsourcing and Crowdtasking in Crisis Management: Lessons Learned From a Field Experiment Simulating a Flooding in City of the Hague[END_REF]11,[START_REF] Auferbauer | Centralized Crowdsourcing in Disaster Management[END_REF] into the overall relief efforts [START_REF] Neubauer | Crowdtasking -A New Concept for Volunteer Management in Disaster Relief, Environmental Software Systems[END_REF]: spontaneous contributions of individual citizens, requests for contribution that originate from the crisis manager and are then executed by volunteers or even the integration of existing groups for guidance and support. During the Trial, CrowdTasker was used to coordinate pre-registered volunteers as well as to evaluate computer-supported interaction with spontaneous, emergent groups that are not registered. Pre-registered volunteers were tasked with confirming observations on site both via the smartphone application and a chatbot interface. Emergent groups were guided in their activity as well as supported in coordinating amongst each other to achieve more complex goals such as setting up tents, providing drinking water, picking up medical equipment from a specific spot, etc.

Airborne Terrestrial Situational Awareness

Real-time aerial imaging significantly enhances situational awareness [START_REF] Römer | Using airborne remote sensing to increase situational awareness in civil protection and humanitarian relief -the importance of user involvement[END_REF] during major and large-scale disasters. DLR's (German Aerospace Centre's) solution "Airborne and Terrestrial Situational Awareness" comprises of several modules (see also Fig. 4) to provide such a real-time aerial imaging and analysis system. Module 1 is the ground control station U-Fly, used to plan, engage and monitor aerial missions. The full-size research aircraft D-CODE, which is operated as a drone demonstrator with safety pilots on board, allowing drone-based missions to be executed without regulatory restrictions or safety concerns, executes the missions.

Module 2 is the 3K aerial camera system, specifically developed to acquire and evaluate aerial photographs in near real-time. In addition, it can transfer aerial imagery via data link directly from the aircraft to a mobile ground station to provide the data to decision makers and rescue forces immediately.

Module 3 is the centre for satellite-based crisis information, which analyses aerial imagery and generates crisis information maps.

Module 4, called KeepOperational, has traffic analysis and route planning capabilities.

Within Trial-Austria, the ground control station U-Fly and the 3K system were selected to demonstrate their capabilities. U-Fly was used to create aerial missions based on the request of the operational commander. These can either be missions to assess larger areas of a (simulated) crisis or to monitor and investigate certain points of interest. The 3K system provided live aerial images of these missions to U-Fly to support an assessment of the overall situation on the ground.

During the execution of different scenarios in the Trial, the operational commander requested aerial images of a certain area by informing the U-Fly remote pilot. The remote pilot created a mission based on this request and activated the mission. The drone demonstrator, equipped with the 3K system, collected aerial images of the area and sent them to the ground immediately. The geo-referenced aerial images were displayed in U-Fly in near real-time and was provided to practitioners and other solutions (e.g. vieWTerra Evolution) via the Test-bed.

vieWTerra Evolution

VWORLD's vieWTerra Evolution [START_REF] Who | War Trauma Foundation and World Vision International[END_REF], vieWTerra Base, vieWTerra Mobile form a combined "GIS & Simulation" suite of products allowing responders to rapidly build a virtual 4D representation (3D synthetic environment + time dimension) of any potential crisis area on Earth. These solutions provide a Common Operational Picture to both the crisis centre and the rescue units out in the field. vieWTerra Evolution is a 4D earth viewer as well as a data & assets integration and development platform. It presents an ellipsoidal model of the earth allowing its users to integrate their own precise datasets anywhere on the globe, without any area coverage limitations, or to access data streams (e.g. imagery, cartography layers).

It can be used to model any type of 3D scene on earth and create scenarios at their real-world location to simulate events in the crisis preparedness phase, and to serve as a global repository for building a custom earth-wide GIS, either used perfectly off-line or ported on to an on-line architecture in order to allow sharing of multiple information, data and assets from disparate sources between all stakeholders in the crisis response phase (3D entities, icons, shapefiles, geotagged reports, photos, videos, sound, multiple overlays such as disaster maps, heat maps, tactical situation, etc.).

Within Trial-Austria, it was used to instantly visualize newly-acquired imagery from drone acquisition and photos taken from the field, shared in real-time with the crisis centre and displayed into/mixed with the 3D view. vieWTerra Mobile complementary software allows display of the same data & assets database in a plug-in free webbrowser-based HTML5 app.

vieWTerra Evolution was used as the Trial's data sink, which means that all information output from each solution was provided via the Test-bed technical infrastructure to vieWTerra Evolution where it was visualized to support the decision-making process of the crisis managers.

ASIGN

ASIGN is a solution by AnsuR Technologies [22] that helps reducing critical emergency and disaster response time. It is a complete all-in-one disaster assessment software tool for easy collection, optimal communication and effective management of operationally relevant critical information. ASIGN supports collection and communication of photos, videos, geo-texts, tracking, geo-zones, geo-alerts and assessment forms in a very bandwidth-efficient manner. Specifically, it can communicate photos and video with 99% bandwidth reduction, enabling communication even through low bandwidth cellular and satellite communication networks while maintaining full precision and accuracy. While the ASIGN Apps work perfectly with regular mobile networks, it also supports satellite communication (via Inmarsat BGAN [START_REF]Presentation of the BGAN satellite communication device[END_REF]) to be used when needed.

ASIGN is comprised of the ASIGN Server, a cloud-based platform from which the incoming information is managed, plus the field user applications ASIGN PRO and UAV-ASIGN, which collect and send information from the field to the server, all with end-to-end encryption. With up to 99% saving in cost and capacity, ASIGN photos and videos from the field can arrive 100x faster at their coordination centre destination.

ASIGN has been actively developed with, and used in the field by, the United Nations, police forces and civil protection entities.

During the Trial the main tasks of ASIGN were: marking of dangerous zones (especially usage of geo-zones and geo-alerts functionality); Communication of photos, videos, text and tracks using smartphones with ASIGN software applications; User and team management plus analysis of incoming data using the ASIGN server; Geo-spatial photo and video clip communication, with mapping integration, for providing improved visual situational understanding; Use of 360 0 videos in addition to regular photos; Use of satellite communication via BGAN [START_REF]Presentation of the BGAN satellite communication device[END_REF] where little bandwidth was available or no internet connection was possible by mobile devices (e.g. smartphones).

Fig. 8. ASIGN modules

PFA -Psychological First Aid

The psychological first aid (PFA) training (provided by the Danish Red Cross -as a non-technical solution) for spontaneous volunteers is a one-day training course, in which participants learn and get to practice the main skills needed to give good PFA in a crisis situation. They learn the internationally recognized principles of Look Listen Link, developed by the World Health Organization (WHO) [START_REF] Who | War Trauma Foundation and World Vision International[END_REF]. The training includes sessions on these three principles as well as role plays, discussion sessions, sharing of knowledge and experience between the participants. Organizations responding to a crisis can implement the training to leverage the resources that spontaneous volunteers bring to a crisis in a positive and safe way.

The leadership seminar for engaging with spontaneous volunteers in crisis response combines the WHO's Look Listen Link principles for PFA, knowledge of caring for volunteers and theory on power relations to build the skills of field level leaders working for crisis response organizations to engage with spontaneous volunteers during crisis response. Through a series of exercises, analytical sessions, roleplays and discussion and reflection sessions, the participants activate their own experience and knowledge and learn from each other to be better placed to engage positively and constructively with spontaneous volunteers.

The ultimate goal of the solution is to alleviate human suffering and foster resilient societies.

The training contributes to this goal by supporting crisis management organizations' staff, so they can engage positively with spontaneous volunteers, and by building the capacity of PFA providers to deliver quality support.

Psychological first aid is a method of helping people in distress, so that they feel calm and supported in coping with their challenges. It is a way of assisting someone to manage their situation and make informed decisions. The basis of psychological first aid is caring about the person in distress. It involves paying attention to the person's reactions, active listening and, if needed, providing practical assistance, such as problem solving or help to access basic needs.

Both the leadership seminar as well as the PFA training for spontaneous volunteers where given during the Trial-Austria and practiced in different exercises throughout the course of the Trial. A first analysis of the feedbacks given for Trial-Austria was that commanders rated the exchanged and provided information as very valuable, impressive and innovative regarding decision making in the view of Crisis Management. All practitioners were impressed by the engagement and spirit of the solution providers working together in the Trial-Austria. One of the statements was "I got in contact with great experts of different countries' professionals and researchers from all over EU!".

Solution providers found it very valuable to see practitioners interacting with the operators of the solutions, on the one hand commanding them (learning the commander's speech) and on the other hand getting immediate recommendations and insight what functionality could be improved or was missing in their solution. This helped each solution provider to identify room for improvements and implement additional functionalities.

All outcomes (e.g. feedbacks to the Trial from Trial observers, Trial practitioners, Trial committee, etc.) as well as lessons learnt will be reported in an Evaluation Report Deliverable by the end of the project (in April 2020).

For now, we can only provide some first initial lesson learnt statements:  TGM has proven to be valuable support tool for setting up Trials.  Setting up a Trial as a field exercise (e.g. including the testing of field components) can quickly become an enormous organisational and logistical challenge. Reducing the number of solutions to be tested would help to reduce the overall complexity. The same is true for testing solutions in parallel.  The Test-bed technical infrastructure has proven stable in the way that all solutions could connect and exchange the requested information and data (e.g. images from the DLR solutions, videos from AnsuR solutions, messages from AIT's solutions) in the needed timeframe. Still, the adaptation work for the solutions to connect to the Test-bed as well as to implement use-cases for the Trial scenario and to test them all is very time consuming.  Installation of international observers and practitioners needs a lot of preparation, coordination and training efforts.  Support tools (e.g. Observer Support Tool, Trial Management Tool, etc.) are very valuable when it comes to evaluation of questionnaires and performance of solutions, but the time needed to prepare all the questionnaires related to injects played within the scenarios and sub-scenarios should not be underestimated.  … more lessons learnt, and recommendation will be provided in the Evaluation Report Deliverable. So please keep track on the DRIVER+ website [START_REF]DRIVER+ Trials[END_REF] on all upcoming reports and deliverables. Finally, to mention is that all solutions tested in the Trials can be found in DRIVER+ Portfolio of Solutions (PoS) [START_REF]DRIVER+ Portfolio of Solutions[END_REF][START_REF] Ignjatović | The Portfolio of Solutions[END_REF]. It is an online catalogue to access information about innovative solutions for Crisis Management. For each solution, practitioners can share their user experiences and solution providers can give background information and offer support. The PoS is currently being scaled up and has the ambition to become the leading platform and one-stop-shop for Crisis Management solutions in Europe.

Acknowledgement

This project has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement n° 607798. The information and views set out in this publication are those of the author(s) and do not necessarily reflect the official opinion of the European Union.

This Trial owes a lot to the professionals of the Austrian Red Cross acting as commanders during the Trial as well as the link to the IRONORE2019 [9] exercise, which brought their professionalism, suggestions and recommendations for further improvements to the Trial, and demonstrated a very impressive and gratifying commitment in the management and execution of the various Trial scenarios but also in the feedback discussion that followed.

The observers, with their professional experience as commanders, teachers and evaluators to this Trial brought a precious methodological perspective for the evaluation, as well as an encouraging feedback.

The students and teachers from the Young Business School in Eisenerz (BHAK) [16] acting as volunteers for the emergent group scenarios testing the CrowdTasker

Fig. 1 .

 1 Fig. 1. DRIVER+ Test-bed technical infrastructure[START_REF]DRIVER+ Test-bed specification[END_REF][START_REF] Vullings | An interoperability Framework for Trials and Exercises[END_REF]

Fig. 2 .

 2 Fig. 2. DRIVER+ Test-bed software for developers and system administrators  Docker environment: Part of the DRIVER+ website on which you can select the Test-bed components to be installed. It creates one installer containing the Docker images of all the selected components, such that these can be easily installed in one go.  Replay Service: Developer component to send out a set of pre-recorded messages across one or more KAFKA topics. Can also be used to demonstrate solutions in a realistic context.  Administrator Tool and security: Developer component to set-up and manage the KAFKA topics and security needed in the CIS and CSS for a specific Trial.  Message Injector: Developer component to quickly send out a message on one KAFKA topic.  Gateway: Translates messages from CSS to CIS and vice versa. CIS messages are standardized for use in emergency services communications. CSS messages are optimized for massive throughput and quick handling by simulators.  Data Services: A set of complementary services to support the Trial, e.g. for storing large data sets, a height model, data from a flooding simulator, a set of fictitious resources, points of interest, map layers, etc.

Fig. 3 .

 3 Fig. 3. Emergent groups organizing themselves with CrowdTasker Application

Fig. 4 .Fig. 5 .

 45 Fig. 4. DLR solution modules used in Trial-Austria

Fig. 6 .Fig. 7 .

 67 Fig. 6. VWORLDs vieWTerra Suite

Fig. 9 .

 9 Fig. 9. Psychological first aid and leadership training held by the Danish Red Cross as a non-technical solution in Trial-Austria

Fig. 10 .

 10 Fig. 10. Solutions' interactions via Test-bed

application and taking part in the PFA training and providing their valuable feedbacks on solutions as well as on the trainings.

We would like to express our gratitude to all of them.


Bernardo Maestrini
email: bernardo.maestrini@wur.nl

Matthijs Brouwer

Thomas Been

Lambertus A P Lotz

Can we use the relationship between within-field elevation and NDVI as an indicator of drought-stress ?

Keywords: Precision agriculture, potato, Netherlands, within-field variability, NDVI, drought, elevation, DEM, AHN2, Actueel Hoogtebestand Nederland

Large farmers' datasets can help shed light on agroecological processes if used in the context of hypothesis testing. Here we used an anonymized set of data from the geoplatform Akkerweb to better understand the correlation between within-field elevation and normalized differential vegetation index (NDVI, a proxy for biomass). The dataset included 3249 Dutch potato fields, for each of which the cultivar, the field polygon, the year of cultivation and the soil type (clay or sandy) was known. We hypothesize that under dry conditions such correlation is negative, meaning that the lowest portions of the field have more biomass because of water redistribution. From the data, we observed that in dry periods, such as the summer of 2018, the correlation was negative in sandy soils. Furthermore, we observed that early cultivars show a weaker correlation between NDVI and elevation than late cultivars, possibly because early cultivar escape part of the long dry summer spells. We conclude that the correlation between NDVI and elevation may be a useful indicator of drought stress, and deviations from the norm may be useful to evaluate the resistance to drought of individual cultivars.

Introduction

The analysis of farmers data is an important opportunity to advance our knowledge of the ecology of agrosystems. One of the most promising approaches to synthesize these data is the use of data-driven approaches where the data drive the construction of models for predictive purposes [START_REF] Liakos | Machine learning in agriculture: A review[END_REF]. Though this approach is powerful for predictive purposes we believe that such large datasets can also be successfully used in the context of traditional hypothesis confirmation studies. Here we seek confirmation in the data for the hypothesis that within-field NDVI variability is drivenalsoby the microtopography (elevation). We do this using a large set of data obtained from "Akkerweb" a geoplatform popular with Dutch farmers (2,akkerweb.eu).

Our main working hypothesis is that the lowest portions of a field are generally more wet and therefore crop growing there is less water-limited. This hypothesis is not novel, for example Kravchenko et al. [3,[START_REF] Kravchenko | Correlation of corn and soybean grain yield with topography and soil properties[END_REF] found that yield variability at within-field level in corn and soybean fields in the Midwest of the US was associated to topography. Timlin et al., [START_REF] Timlin | Spatial and temporal variability of corn grain yield on a hillslope[END_REF] found in the context of a rainfed maize experiment that in dry years the concave parts of the field were more productive than convex parts whereas the effect of terrain curvature disappeared in wet years. Da Silva et al. [START_REF] Da Silva | Spatial variability of irrigated corn yield in relation to field topography and soil chemical characteristics[END_REF][START_REF] Da Silva | Evaluation of Maize Yield Spatial Variability based on Field Flow Density[END_REF] found that in a Portuguese irrigated maize field, characterized by a high elevation range, topography was driving within-field yield variability and showed that maize yield was higher near the flow lines. These findings on yield-topography correlation were extended by Maestrini and Basso [START_REF] Maestrini | Drivers of within-field spatial and temporal variability of crop yield across the US Midwest[END_REF] who observed that not only the yield average but also yield temporal variability (stability) was influenced by topography, with the more concave portions of the field being characterized by a higher variability as a result of being either waterlogged in wet years or relatively more wet in dry years.

Here we attempt to prove that elevation is a driver of within-field crop-growth variability even in the Netherlands, a land that is notoriously flat. Further we extend these findings by examining how different soil types and cultivar earliness influence such correlation. We believe that the correlation between NDVI and elevationonce sufficiently corroborated with datacould be used as an indicator of drought stress and deviations from such correlation may help us understand which management strategies (e.g. cultivar choice) alleviate drought stress.

In this study we focus on potatoes,a major crop in the Netherlandswell represented in our farmers' dataset. Potatoes have been generally regarded as a crop with a shallow root system [START_REF] Haverkort | Plant[END_REF][START_REF] Lesczynski | Seasonal variation of root distribution of irrigated, field-grown Russet Burbank potato[END_REF] that makes them vulnerable to drought stress, however there are empirical evidences that their roots can reach one meter [START_REF] Vos | Root growth of potato crops on a marine-clay soil[END_REF] and beyond [START_REF] Scott | Principles of agronomy and their application in the potato industry[END_REF].

Therefore, here we hypothesize that also for the Netherlands we can establish a correlation between relative within-field elevation and crop-growth and that such correlation is influenced by both soil type and cultivar earliness. We expect that potato crops on sandy soils as well as late cultivars are more sensitive to dry spells. The sandy soils will be more sensitive because they have a low water holding capacity whereas late cultivars will suffer more when exposed to summer dry spells for longer periods. We apply our hypothesis to a dataset of Dutch farms where the location of the field, the cultivar, the soil type (clay or sandy) is known and the biomass throughout the season is proxied by the normalized difference vegetation index (NDVI) retrieved from satellite images, is known, along with elevation.

Materials and methods

Data from Akkerweb

Data were collected from farmers' data input to the geo-platform Akkerweb [START_REF] Evert | Akkerweb: a platform for precision farming data, science, and practice[END_REF] (akkerweb.eu). Through this platform farmers can enter the information about their fields (polygon, crop and cultivar, and sowing dates) and receive information to support their decision making onamong othersin-season fertilization, crop protection, pests like plant-parasitic nematodes and variable rate applications like haulm killing for potatoes.

We received the anonymized data, supplied by the farmers to the Akkerweb geoplatform, through a query that returned the dataset records for which the cultivar, the start and end date of the management plan, and the polygon were available. The query returned the following fields: a key identifier for each field, the field polygon (in wellknown text format), the area, the cultivar, the purpose (e.g. for potato consumption, starch, table or seed potatoes), start and end date of the management plan and soil type. The total number of unique records returned was over 100k. We produced a subset of the dataset where the crop was potato, the purpose was not production of seed potato and the field area was larger than 2 ha and smaller than 100 ha. The fields smaller than 2 ha were excluded because often after the removal of a 10 m buffer there wouldn't be enough pixels left to perform the analysis (particularly if they were characterized by an elongated form). The fields were cultivated in the years 2015 to 2019. Here we will focus on the analysis of the years 2016, 2017 and 2018 because these years are representative of very different weather conditions, particularly 2016 was a wet year, 2017 was a dry spring and a wet summer, and that 2018 was characterized by a very dry summer.

For this analysis we used only a randomly selected subset of records of 3249 fields located on soils classified either as sandy or clay, cultivated with potato (excluding seed potatoes) in the years 2016, 2017 and 2018.

Publicly available data

For each field we retrieved the images from the satellites Landsat 8 and Sentinel 2. For the years before 2017 we only retrieved images from Landsat 8. For each field we also used information from the Dutch digital elevation model (DEM) Actueel Hoogtebestand. This digital elevation was produced using airborn lidar data collected between 2007 and 2012 (AHN2). We used the version of the dataset interpolated at a resolution of 0.5 m. This DEM has "an accuracy of 20 cm for 99.7% of the points. The average point density for AHN2 is between 6 and 10 points per square meter" [START_REF]Actueel Hoogtebestand Nederland De details van het Actueel Hoogtebestand Nederland[END_REF].

For each cultivar we tried to retrieve data on the cultivar performance, e.g. earliness score or susceptibility to late blight, reported by the cultivar vendor and available on the Akkerweb platform. The performance of individual cultivars are usually scored on a number of indicators on a scale from 1 to 9 (for earliness 1 corresponding to the latest varieties and 9 to the earliest varieties). In this context we used the score on cultivar earliness to make inferences about the influence of the cultivar earliness on the correlation elevation-NDVI.

We used Google Earth Engine [START_REF] Gorelick | Google Earth Engine: Planetaryscale geospatial analysis for everyone[END_REF] to perform calculations on satellite images (Landsat 8 and Sentinel 2) and the Dutch DEM (AHN2 interpolated to 0.5 m). For each polygon we applied the following algorithm (pseudocode):

1. Import the polygon to Google Earth Engine. The information about the polygon were passed in the form of a list of coordinates. 2. Remove a 10 m buffer from the border to make sure that we did not include pixels that were not heterogenous in land cover (e.g. half road and half field). 3. Check the images available in the Landsat 8 and Sentinel 2 surface reflection collection. 4. Remove the clouds and cirrus from Sentinel 2 and clouds and cloud shadows from Landsat 8 using the respective pixel quality bands. 5. Clip the raster regions to the clip polygon setting to "not available" the pixels out of the field. 6. Calculate for each image the following quantities were calculated through a reducer function: a. Standard deviation of elevation. b. Sperman correlation between NDVI and elevation. To calculate the correlation the elevation dataset was resampled to match the resolution of the vegetation index dataset because this was coarser than the DEM(0.5 m vs 30 m for Landsat 8 and 10 m for Sentinel 2).

If an image was completely covered by clouds, cirrus (for Landsat 8) or cloud shadows (for Sentinel 2) the calculated quantities were set to "not available". The cloud coverage in the images was calculated from the quality band pixels in the two satellites (namely pixel_qa for Landsat8, and QA60 for Sentinel2).

We retrieved cumulative rain from weather data using the set of weather stations of the Royal Netherlands Meteorological Institute (KNMI, 45 stations). For the purpose of this study we calculated an average Dutch weather for the Netherlands by averaging the values of all the weather stations (Figure 1 left).

Fig. 1.

Here we represent the correlation among over the growing season for the years 2016 to 2018 in contrasting soil types (clay vs sandy). The continuous lines represent the average measured across all the fields in a certain date. The dashed lines and the colored area represent the prediction and the 95% CI interval of the mean calculated using a 3 rd degree polynomial equation.

To approximate the temporal variability of correlation between NDVI and day of the year (DOY) we fitted a 3 rd degree polynomial model to the data. This was intended more as a tool to visually interpolate the data rather than a predictive tool or an inferential tool.

Descriptive statistics on the retrieved dataset

The cultivars present in our subset for which we performed calculations on satellite images are presentedanonymizedin Figure 2 along with the earliness score.

Results

The correlations that we found were generally weak as the absolute value of the Spearman correlation was on average well below 0.2, but thanks to the high number of observations we were to observe ecologically meaningful trends in them. We found that NDVI was negatively correlated with elevation in sandy soil and dry periods (spring 2017, summer 2018), whereas such negative correlation was not observed for clay soils in all the years and in sandy soil for 2016 (a wet year, Figure 1). In clay soils the correlation was positive at the beginning of the season whereas no correlation was observed on average for the rest of the season.

Also, the earliness score of the cultivar influenced the correlation. In fact the early varieties showed a neutral or positive correlation in wet years (indicating that higher biomasses were observed at relatively higher location), whereas the late developing varieties indicated a predominance of negative correlation in the dry years (Figure 5).

Discussion

The correlations that we observed between the Normalized Difference Vegetation Index (NDVI, a good proxy for biomass) and elevation were generally weak suggesting that their use for prediction purposes at within-field scale is limited, nonetheless on a larger scale the correlation elevation-NDVI may be a useful indicator of cultivars sensitivity to drought stress. We observed that the correlation was negative in dry periods in sandy soils, whereas we hardly observed a negative correlation between within-field elevation and NDVI in clay soils (Figure 1). The negative correlation for sandy soils may be easily explained by within-field water rerouting and/or distance from the water table and has generally been reported before for different crops (see introduction). The lack of correlation in the clay soils may be explained by geographical position of clay soils. In fact, in our dataset clay soils exhibit the lowest within-field elevation variability because they are mostly located in reclaimed areas (Flevoland province, Figure 4) and are notoriously very flat. Moreover, clay soil has a higher water holding capacity and is therefore less prone to induce drought stress in the crops, and it has been shown that roots of potatoes in clay soils may grow as deep as one meter possibly because their growth can be facilitated by the presence of cracks in the subsoil [START_REF] Vos | Root growth of potato crops on a marine-clay soil[END_REF].

Interestingly we observed a weak positive correlation in the clay at the beginning of the season, irrespective of the year. This could be due to the fact that the higher portions of the fields are less wet and thus warmer at emergence and as a consequence they develop more rapidly.

Our data on the within-field correlation between elevation and NDVI also offer a first insight on how large data from farmers could be used to evaluate differences between cultivars. An evaluation of this correlation for individual cultivars goes beyond the scope of this small study as it would require an evaluation of the performance of the different cultivars under drought for validation. Such scoring is not available to us at the present stage. However, we were able to evaluate how the cultivar earliness score influences the correlation in sandy soils. We found that late cultivars had a stronger negative correlation between NDVI and elevation than early ones. We suggest that early cultivars may escape dry spells because in the summer they have already developed deeper roots, nonetheless we have not yet data to validate this hypothesis.

Limitations of this study

An important factor which we did not consider in this study was irrigation. It is likely that irrigation strongly influenced the correlation between NDVI and elevation, possibly also in unexpected direction. It could be that higher water availability levels-out the differences between the different parts of the field (as the difference in the correlation between wet and dry years would suggest) or it could also be that surface water rerouting exacerbates such differences. We were not able to investigate this aspect with the current dataset, but we believe that information on elevation could be useful to drive precision irrigation. As we said this methodology has the potential to obtain information about cultivar differences for drought sensitivity. However to be usefully deployed such capability should be made available to breeders, whose plots are too small to be sensed using Landsat or Sentinel. Nonetheless the deployment of new satellites (e.g. Worldview) with finer resolution can open important opportunities in this direction.

An important aspect is that the AHN2 dataset has an accuracy (20 cm) that is lower than the field elevation in many cases. This would lower our correlation between vegetation indices and elevation and therefore not impair the validity of our correlation estimates.

Conclusion

Using farmers' data we were able to observe a negative correlationalthough weak between NDVI and elevation, and show how this correlation is stronger during dry periods. Because the correlation between the two variables was generally low, elevation has limited predictive power for within-field variability of growth in the Netherlands.

Nonetheless we were able to depict how cultivars earliness influences such correlation. The correlation between altitude and vegetation is a parameter that can be virtually measured for every crop and deviations from this parameter, when measured over sufficiently large samples may carry important information about agroecological processes such as cultivars sensitivity to drought-stress.

Fig. 2 .

 2 Fig. 2. Number of fields for the cultivars (anonymised) by year used in this study. The total number of fields analyzed was 3249

Fig. 3 .

 3 Fig. 3. Geographical distribution of clay and sandy soils in the fields represented in this study.

Figure 4

 4 Figure4shows the distribution of the within-field variability of elevation in the Netherlands. It is well known that fields in the NL are generally very flat. The most uneven fields are located in the western part of the country and in the southern province of Limburg (median standard deviation of elevation being 20 cm) whereas as expected the fields in the region of Flevoland, reclaimed from the sea in the sixties and seventies of last century were the most flat (median standard deviation of elevation typically < 10 cm).

Fig. 4 .

 4 Fig. 4. Median of the standard deviation of the elevation within-fields across the Netherlands. The grey dots represent the field randomly sampled in this study.

Fig. 1 .

 1 Fig. 1. These figures refer to the NDVI-elevation correlation observed in the different years of late cultivars (earliness score 1 to 5) and early cultivars (earliness score 5 to 9) over the course of the season in three different years (2016, wet, 2017 dry in the spring and 2018 dry in the summer). The colored areas indicate the confidence interval of the mean predicted using a third degree polynomial. The black line indicates cumulative rainfall (referred to the first right axis) and the dashed lines represent the median NDVI over the season (referred to the left axis).

Acknowledgements

The project was funded by the internal funds of WUR-Agrosystems Research. We are grateful to the Akkerweb Foundation for providing the anonymized dataset and to the two referees for their insightful comments.