Geodata tools have been developing rapidly in the past years and are vastly adopted by researchers and increasingly by policy-makers. However, the is still great potential to increase the practical application of these tools in the agricultural sector, which is currently applied by a limited number of ‘pioneering’ farmers. The information that can be gained from geodata tools on irrigation management, pest and nutrient management, and crop selection, is a valuable asset for farmers. Key players for providing such information to the farmers are the extensions officers. This project aims at training extensions officers in the use of these geodata tools. The beneficiaries in Egypt are: Tamkeen for Advanced Agriculture, FAODA, IDAM, Bio-Oasis, and LEPECHA. The selected participants will receive a training programme which consists firstly of several session on the background and theory of the geodata tools, provided through our online teaching platform (futurewater.moodle.school). Starting from May (2021) field schools will be set up to use the geodata tools for decision-making in these demonstration plots. In addition, modules are taught on the quality of the data, and profitability of such tools. Altogether, a group of carefully selected participants will receive training on these innovative tools and create a bridge to providing this information to farmers specifically the smallholder farmers.
Aim of the training
The training will enhance capacity of Egerton educational staff in accessing and using innovative data and tools in the public domain, to analyse crop performance and irrigation management. During the training, university participants will be specifically supported in developing course modules based on the skills gained. To maximize the impact in addressing the need for increased quality of higher education in the agricultural sector, representatives from other institutes, ministries and private sector companies will also be invited. The training will allow the staff to gain advanced skills in working with flying sensors (drones) and satellite-derived data to support agricultural and water-related challenges, such as pests and diseases, water efficiency in agriculture to enhance food security, and drought monitoring. They will acquire insight in and knowledge on analyzing the performance of crops, making the right intervention decisions and giving irrigation advice. For public sector representatives, the training objective is to obtain skills that can be directly and sustainably implemented in their respective organizations.
Overall, the Kenyan society at large will benefit from improved food security provided by well-educated agricultural researchers and professionals. This project forms an important step in the capacity building strategy as it focuses on strengthening the universities and preparing them to provide high quality education to the future generation agronomists and agricultural managers, as well as upgrading the knowledge of current professionals.
The training costs of four stages: an online training course, followed by an in-country training program, symposium and post-training support.
Stage 1: eTraining course
The first stage involved a weekly online training course that will start in January 2021, with a total of six sessions in six weeks. Participants will be consisting of University and TVET faculty members, university students, PhD candidates, researchers, Kenya Agricultural & Livestock Research Organization (KALRO) staff members, Agriculture Extension Staff from the County Government who are already involved in agricultural research and training and other private sector partners. Staff members from the university will be those that are involved in teaching agronomy, horticulture, agriculture engineering and agriculture extension courses and programs, i.e., soil, nutrient and water management, dryland farming, irrigated agriculture and crop protection. Non-university attendants will be technical staff who are close to the decision makers within their organizations. This will enhance the impact of the training by embedding the use of flying sensor and satellite-derived data for agriculture within these organizations and make sure that Kenya will pursue its activities in making use of this kind of information.
This first stage of the training course will be online and will focus on:
- Real Water Savings in Agricultural Systems including potential field interventions
- The use of WAPOR to access remotely sensed derived data
- The use of flying sensors (drones) in agriculture
The course will end with a test and evaluation and graduates will receive a certificate.
Stage 2: Targeted in-country training
After the first stage training a second in-country training will take place with a smaller group, focusing on the use of drones in agriculture. Here a selected group of 12 to 18 members will be trained. Focus will be on staff with lecturing responsibilities, to ensure impact on higher education provision and transfer of the new skills to students.
The in-depth training will consist of:
- Operating flying sensors manually and automatic, the processing of the collected data using open source software, interpretation and the subsequent decision making (recommendations to increase productivity) for (smallholder) farmers and actors
- Use satellite derived (precipitation) products to run crop growth models to provide advice on when and how much to irrigate in agricultural fields
Participants will work on hands-on exercises related to crop performance analyses, water demands and crop growth modelling. Application of the new skills will be further stimulated by assigning the participants clear, tailor-made goals at the end of the second training session, to be worked on during the distant-support period.
Stage 3: Symposium/knowledge sharing
Right after the second training session, a symposium will be organized for a larger audience including the superiors/managers (who most of the times are the final decision makers) of the training participants and representatives of similar organizations. During this knowledge sharing event, trainees and trainers will actively provide contributions to showcase the newly gained skills and their added value to the respective institutions and the Kenyan agricultural sector in general. By acquainting the responsible decision makers in these organizations with the potential applications of flying sensor and satellite-derived data relevant to them, this event will be crucial in ensuring a sustainable impact of the TMT.
Stage 4: Post-training support
In this period, progress will be actively monitored and the trainers will provide post-training support to the participants. The support will be both remotely (e.g. through Skype) by the Dutch training providers but also in-person by ThirdEye Kenya staff visiting the participants for Q&A sessions and to evaluate the implementation of the skills they obtained.
The scope of the project work is as follows:
- Train selected NCBA Clusa PROMAC staff on drone operation, imagery processing software, and crop monitoring;
- Provide technical assistance to trained NCBA Clusa staff on drone operation, imagery processing, and interpretation of crop monitoring data;
- Present technical reports on crop development and land productivity (i.e. crop yield) at the end of the rainy and dry season
The trainings and technical assistance for the NCBA Clusa staff are provided in collaboration with project partners HiView (The Netherlands) and ThirdEye Limitada (Central Mozambique). Technical staff of the NCBA Clusa are trained in using the Flying Sensors (drones) in making flights, processing and interpreting the vegetation status camera images. This camera makes use of the Near-Infrared wavelength to detect stressed conditions in the vegetation. Maps of the vegetation status are used in the field (with an app) to determine the causes of the stressed conditions: water shortage, nutrient shortage, pests or diseases, etc. This information provides the NCBA Clusa technical staff and extension workers with relevant spatial information to assist their work in providing tailored information to local farmers.
At the end of the growing season the flying sensor images are compiled to report on the crop development. The imagery in combination with a crop growth simulation model is used to calculate the crop yield and determine the magnitude of impact the conservation agriculture interventions have in contrast with traditional agricultural practices.
For smallholder farming systems, there is a huge potential to increase water productivity by improved (irrigated) water management, better access to inputs and agronomical knowledge and improved access to markets. An assessment of the opportunities to boost the water productivity of the various agricultural production systems in Mozambique is a fundamental precondition for informed planning and decision-making processes concerning these issues. Methodologies need to be employed that will result in an overall water productivity increase, by implementing tailored service delivery approaches, modulated into technological packages that can be easily adopted by Mozambican smallholder farmers. This will not only improve the agricultural (water) productivity and food security for the country on a macro level but will also empower and increase the livelihood of Mozambican smallholder farmers on a micro level through climate resilient production methods.
This pilot project aims at identifying, validating and implementing a full set of complementary Technological Packages (TP) in the Zambezi Valley, that can contribute to improve the overall performance of the smallholders’ farming business by increasing their productivity, that will be monitored at different scales (from field to basin). The TPs will cover a combination of improvement on water, irrigation, and agronomical management practices strengthened by improved input and market access. The goal is to design TPs that are tailored to the local context and bring the current family sector a step further in closing the currently existing yield gap. A road map will be developed to scale up the implementation of those TPs that are sustainable on the long run, and extract concrete guidance for monitoring effectiveness of interventions, supporting Dutch aid policy and national agricultural policy. The partnership consisting of Resilience BV, HUB, and FutureWater gives a broad spectrum of expertise and knowledge, giving the basis for an integrated approach in achieving improvements of water productivity.
The main role of FutureWater is monitoring water productivity in target areas using an innovative approach of Flying Sensors, a water productivity simulation model, and field observations. The flying sensors provide regular observations of the target areas, thereby giving insight in the crop conditions and stresses occurring. This information is used both for monitoring the water productivity of the selected fields and determining areas of high or low water productivity. Information on the spatial variation of water productivity can assist with the selection of technical packages to introduce and implement in the field. Flying sensors provide high resolution imagery, which is suitable for distinguishing the different fields and management practices existent in smallholder farming.
In May 2020, FutureWater launched an online portal where all flying sensor imagery from Mozambique, taken as part of the APSAN-Vale project, can be found: futurewater.eu/apsanvaleportal
The Mashhad city is the second largest city in Iran. The economic growth in the Mashhad city is strongly threatened by water shortages and unregulated groundwater extraction. The situation is critical, and the government is considering drastic infrastructural measures such as desalination and water supply from the Sea of Oman (Ministerie van Landbouw, 2018). Hence, finding cost-effective alternatives to reduce groundwater consumption in the Mashhad basin (Figure 1) is of regional interest.
The SMART-WADI project (SMART Water Decisions for Iran), carried out by a consortium of FutureWater, IHE-Delft, and local partner EWERI, focuses on farmers who irrigate their crops with groundwater. The aim is to provide up-to-date information and advice on water productivity, irrigation and farm management. The project combines the latest satellite technology for the quantification of water consumption and productivity, with high resolution flying sensor (drone) images to monitor the crop growth.
Using this information in a crop model can determine the potential for improving agricultural practices and reduce groundwater consumption. This way, a higher crop yield (food production) and higher water productivity can be obtained (Figure 2). Eventually farmers receive this information in combination with recommendations regarding irrigation planning via an online portal or mobile app.
SMART-WADI is now in the phase of a feasibility project, in which the market context and technical aspects are tested. This is supported by the Partners for Water Program of RVO.nl, with co-funding from the executive project partners. Based on the first signals and the experiences of FutureWater and IHE-Delft in similar projects, it is estimated that this information service has great potential to be scaled up to other areas in Iran.
FutureWater is developing and testing a framework to predict crop yield and water productivity based on crop growth monitoring using flying sensors and remote sensing. Thanks to this innovation, farmers can timely plan field management practices (e.g. irrigation application) enhancing water productivity and reducing groundwater consumption.
Twiga’ is the Swahili word for ‘giraffe’, a keen observer of the African landscape. TWIGA aims to provide actionable geo-information on weather, water, and climate in Africa through innovative combinations of new in situ sensors and satellite-based geo-data. With the foreseen new services, TWIGA expects to reach twelve million people within the four years of the project, based on sustainable business models.
Africa needs reliable geo-information to develop its human and natural resources. Sixty percent of all uncultivated arable land lies in Africa. At the same time Africa is extremely vulnerable to climate change. Unfortunately, the in situ observation networks for weather, water, and climate have been declining since the 1970s. As a result, rainfall predictions in Africa for tomorrow have the same accuracy as predictions in Europe, ten days ahead. To realize the tremendous potential of Africa while safeguarding the population against impacts of climate change, Earth observation must be enhanced and actionable geoinformation services must be developed for policy makers, businesses, and citizens. New in situ observations need to be developed that leverage the satellite information provided through GEOSS and Copernicus (Open data/information systems).
TWIGA covers the complete value chain, from sensor observation, to GEOSS data and actionable geoinformation services for the African market. The logic followed throughout is that in situ observation, combined with satellite observations and mathematical models, will result in products consisting of maps and time series of basic variables, such as atmospheric water vapour, soil moisture, or crop stage. These products are either produced within TWIGA, or are already available with the GEOSS and Copernicus information systems. These products of basic variables are then combined and processed to derive actionable geo-information, such as flash flood warnings, sowing dates, or infra-structural maintenance scheduling.
The TWIGA consortium comprises seven research organisations, nine SMEs and two government organisations. In addition it uses a network of 500 ground weather stations in Africa, providing ready-to-use technical infrastructure.
FutureWater’s main role in TWIGA is centered around the use of flying sensors to map crop conditons, flood extent, and energy fluxes, complementing and improving data from in situ sensors and satellites. Furthermore, FutureWater is involved in innovative app development.
Nowadays, projects that invest in sustainable water management and agriculture require evidence that the targeted measures to boost water productivity are effective. Water productivity monitoring therefore becomes increasingly important. Water productivity requires data on yields and water consumption (evapotranspiration). Yield data are often difficult to obtain from farmers, especially in areas with many smallholders. Evapotranspiration is even more difficult to assess in the field. Remote sensing-based and model-based monitoring of water productivity has a large potential, also to identify yield gaps and assess the local feasible effectiveness of measures.
The objective of this pilot study was to achieve plot-level maps of water productivity and yield to test a methodology to assess the performance of different farmers in order to provide them with recommendations to improve water productivity. More specifically, this pilot study combined high-resolution imagery from Flying Sensors (FS) with a crop water productivity model to assess yield and water productivity for several plots with maize in Mozambique. Canopy cover was derived from the imagery and linked with the crop model simulations to obtain water productivity maps covering the entire growth cycle. The methodology is also used for the monitoring of crop performance during the growth season and can be used to forecast yield by the end of the season.
This feasibility study demonstrated that there is an opportunity to further develop a service that monitors water productivity based on FS-imagery and crop modelling. Service costs outweigh the additional revenues obtained by farmers. The experimental development has demonstrated that the service is technically feasible and can provide the tangible outputs needed. To bring the proposed service to a higher level of maturity, it is recommended to focus future development activities on (i) Testing for different locations and crops, (ii) Further enhancing link between FS-based imagery and crop modelling, and (iii) Involving end-users and testing within a project where WP-measures are implemented.
Substantial methodological progress has been made in identifying potential risks of flooding by improved monitoring and modeling. The number of web-portals and similar information sources has been exploding over the last years (e.g. www.reliefweb.int; www.hewsweb.org; amongst many others). However, this information is often at relatively course resolutions and focused towards high-level decision makers and managers; so leaving the decision makers on-the-ground empty-handed.
Mozambique’s major rivers (Limpopo, Incomati, Zambezi) flood on a regular base with in some years devastating impact (e.g. 2000) creating attention of high-level decision makers. However, floods happen every year in the country and on-the-ground water managers are confronted with big challenges to make decision on controlling and regulating these floods. Especially, the smaller infrastructure such as small dikes and levees are key in controlling and managing risks of the very regular flooding that is not always taking lives directly, but has a huge impact on peoples’ lives. As mentioned in the risk literature: a devastating disaster brings aid, while the recurrent small disasters destroy peoples’ resilience.
Lack of information is considered as one of the major limitations to risk identification to these floods. This lack of information is both on the assessment of vulnerable dikes and levees, as well as the information during floods on appropriate responses and mitigation. Focus of this project is at on-the-ground data and tools to analyze risks.
Flying Sensors’, sometimes referred to as “drone” or “unmanned aerial vehicle (uav)”, technologies have leapfrogged over the last years in terms of technology of the platforms themselves. Flying Sensors are now on the market at very achievable prices that can be used semi-automatically. The real challenge has therefore be shifted from a technically one to the use as operational observation services and to convert the raw imagery into information. Software to operate the Flying Sensors and to analyze imagery is in the public domain.
FutureWater has started a demonstration project in Mozambique to support farmers by providing crop status information based on Flying Sensors. Currently six local extension services staff are certified and are using Flying Sensors to provide high-resolution real-time crop information to farmers. Water managers (regadios and ARAs) in the same region see a huge potential to use a similar approach for risk identification of flooding. In order to do so, different sensors and image analysis procedures are needed. Specifically, current sensors are detecting at the near-infrared spectrum, while for dike and levee monitoring RGB is needed. Also, currently image analysis focusses on crop reflection, while for dike and levee monitoring ultra-high resolution elevation model and acreage detection are needed.
The outputs of this project can be summarized as providing real time dike and levee information at local scales (up to 10,000 ha) that is collected, analyzed and used at the same local scale. The output is divided into two components. First, the Flying Sensor information will be used for risk reduction by monitoring smaller dikes and levees to identify fast and accurate potential weak points. Second, during flood events Flying Sensors will be used to monitor flood extent and detection of location of flood cause. Focus of the project will be on the Risk Identification component.
The outcome will be that local water managers and decision makers make better and faster decisions regarding risk reduction by monitoring potential weak points in small dikes and levees; and during floods make the right decision disaster response like evacuation, rescue operations, and damage control.
The final result of the project will be that two water management institutions (RGD in Xai-Xai and ARA-Sul in Beira) can obtain by themselves Flying Sensor information on flood risk reduction and flood management. This will be achieved by training four Flying Sensor operators (undertaking flights, collect information, imagery analysis and interpretation). The feasibility of such an approach has been demonstrated by the on-going crop monitoring and advisory services by the extension services in the same region. Moreover, the use of Flying Sensors has a high attractiveness and has resulted already in cross-linkages between public, private and research entities in the country.
The longer-term projection is that other water managers and decision makers will see the benefits of the use of Flying Sensors in their disaster risk reduction strategy. The main “selling-points” will be: (i) affordable, (ii) access to flooded areas, (iii) local collected and local used, (iv) huge scaling potential.
A key factor in enabling an increase and efficiency in food production is providing farmers with relevant information. Such information is needed as farmers have limited resources (seed, water, fertilizer, pesticides, human power) and are always in doubt in which location and when they should supply these resources. Interesting is that especially smallholders, with their limited resources, are in need of this kind of information. Spatial information from flying sensors (drones) can be used for this. Flying sensors offer also the opportunity to obtain information outside the visible range and can therefore detect information hidden for the human eye (Third Eye). Nowadays, low-cost sensors in the infra-red spectrum can detect crop stress about two weeks before the human eye can see this.
The ThirdEye project supports farmers in Mozambique and Kenya by setting up a network of flying sensors operators. These operators are equipped with flying sensors and tools to analyse the obtained imagery. Our innovation is a major transformation in farmers’ decision making regarding the application of limited resources such as water, seeds, fertilizer and labor. Instead of relying on common-sense management, farmers are now able to take decisions based on facts, resulting in an increase in water productivity. The flying sensor information helps farmers to see when and where they should apply their limited resources. We are convinced that this innovation is a real game-changing comparable with the introduction of mobile phones that empowered farmers with instantaneous information regarding markets and market prices. With information from flying sensors they can also manage their inputs to maximize yields, and simultaneously reduce unnecessary waste of resources. In summary, the missing information on markets has been solved by the mobile phone introduction, the flying sensors close the missing link to agronomic information on where to do what and when.
Thanks to our innovation, farmers’ demand for key agricultural information will be satisfied by means of an extension service based on flying sensor (drone) information. The deployment of flying sensors is unique in its ability to provide farmers with real-time, high-resolution, and on-demand information. We provide essential agricultural information:
- At an ultra-high spatial resolution (NDVI)
- With unprecedented flexibility in location and timing
- Based on wavelengths not observable by the human eye
- With a country-specific business oriented approach.
To this end, we use low-cost high-resolution flying sensors (drones) in a development context to ensure that farmers will get information at their specific level of understanding, and simultaneously develop a network of service providers in Mozambique and Kenya.
A flying sensor is a combination of a flying platform and camera. Reliable flying sensors are on the market in a wide-range of categories each with its specific characteristics. Based on the consortium’s experiences over the last years low-cost flying sensors have been identified that are excellent equipped for our innovation. Typically, a flying sensor flies at a height of 100 meter and overlapping images are taken about every 5 seconds. This results in individual images covering about 50 x 50 meter and an overlap of 5 images for each point on earth. So, in order to cover 100 ha 500 images are taken during a flight.
The use of Flying Sensor is unique and no comparative techniques exist that provide farmers with real-time high-resolution information. The use of satellites to provide farmers with spatial information has been promoted but has three main limitations: they have fixed overpass times, the spatial resolution is low, and the presence of clouds halters the information. It is unlikely that, within the coming decades, progress in satellites will be real competitors of flying sensors. Another category of comparable techniques to provide farmers with information is the use of ground sensors. Typical examples of these sensors are soil moisture devices, soil sampling and laboratory analysis, crop sampling and laboratory analysis. However, all those sensor techniques have the common limitation that information is only local point representative, while the main question farmers have is regarding to spatial differences. Moreover, these ground sensors are in all cases too expensive to be used by small-scale farmers.
Our flying sensors have cameras which can measure the reflection of near-infrared light, as well as visible red light. These two parameters are combined with a formula, giving the Normalized Difference Vegetation Index (NDVI). This information is delivered at a resolution of 2×2 cm in the infra-red spectrum. Infra-red is not visible to the human eye, but provides information on the status of the crop about two weeks earlier than what can be seen by the red-green-blue spectrum that is visible to the human eye. NDVI is the most important ratio vegetation index and says something about the photosynthesis activity of the vegetation. Moreover, NDVI is an indicator for the amount of leaf mass, and therefore, ultimately biomass. In general, open fields have a NDVI value of around 0.2 and healthy vegetation of around 0.8. NDVI values give an indication of crop stress. This can be caused by a lack of water, lack of fertilizer, pests or abundancy of weeds.
When light falls on a leaf, reflection occurs. The amount of reflection of green light (0.54 µm) is very high, making plants green to the human eye. Healthy vegetation does not reflect much red light (0.7 µm), since it is absorbed by chlorophyll abundant in leaves. In the near-infrared spectrum (0,8 µm) the amount of reflection increases rapidly to 80% of the incoming light. This increase is caused by the transition of air between cell kernels. This is characteristic for healthy vegetation.
Damaged plant material does not show this increase in reflected near-infrared light. Moreover, the reflection of red light is much higher than in healthy plant material. By measuring the reflection in these spectra, damaged plant material can be distinguished from healthy plant material (Schans et al., 2011).
From 2014 to 2017, FutureWater has been granted support from the Securing Water for Food program, funded by USAID, Sida and the Dutch Government of Foreign Affairs, for piloting the use of flying sensors to support farmers in Mozambique with their decision making in farm and crop management. In Mozambique, we have transferred our technical skills to local ThirdEye operators over the past 3 years. We currently have 6 active local operators providing service to more than 3,500 farmers over more than 1,600 ha. These operators are able to support over 400 small-scale farmers, by collecting information and sharing it with farmers on weekly basis. Based on the information, farmers take decisions on where to do what in terms of irrigation, fertilizer application and pesticides, helping them improve their water productivity. Furthermore, they now have the capacity to deal with technical issues and are very skilled in providing advice to farmers. As a result, the farmer’s water productivity was increased by 55%, meaning less water is used to achieve the same crop yield as without ThirdEye services. ThirdEye has evolved since 2014 from a start-up to becoming the leading company in Mozambique in the field of mapping and monitoring services for farmers based on aerial images, which will continue to expand its activities over the coming years.
Since last year, the ThirdEye service is also implemented in Kenya as part of the Smart Water for Agriculture program implemented by SNV. Last month the first round of training was given to 5 operators, who will be serving at least 2,000 smallholder farmers the coming months. Training consists of flying sensor use, technical skills, safety and protocols, imagery processing and consultancy. After this, the operators will start working regularly in the regions of Meru and Nakuru. Here they will go the farmer’s fields, conduct flying sensors flights, process the images and give advice on improving their agricultural practices. Next to the service for smallholder farmers, ThirdEye delivers various services to medium and big sized farmers.
- Project website
- Project video
- Follow us on Twitter
- Flyer of the project
- Project description (in English)
- Project description (in Portugese)
Water supply and sanitation in the United Kingdom is modern and of good quality. However, increasing pressure on freshwater resources is a major concern to UK. Water is needed for irrigation, forestry, industrial users, environmental flows for country’s river systems, and for human consumption. With increasing pollution and economic growth, these pressures will continue to increase over the coming decades, making it more important to have accurate information about the sources and availability of water, as well as the consumption of water such as monitoring the amount that is pumped from the groundwater sources.
The assessment of water quality (nitrates, pesticides, bacteria, salt, and algae) in the distribution networks is highly relevant for public health and the estimation of industrial and natural sources and impacts is a priority. The improvement of the water availability estimation (stored in soil and reservoirs) is crucial, in order to improve the planning of distribution and the forecast of possible floods and droughts. Also, the infrastructures such as pipes of drinking water and sewage are critical elements, and the improvement of monitoring could allow faster response time and leading to easier maintenance in case of disruptions. Water managers and planners have been using data from satellite sensors to support catchment level monitoring and forecasting, to estimate parameters such as assess the impact of land use change on water availability. Earth Observation data (EO) is increasingly being used for compliance monitoring.
EO is an important enabling technology for the better management and accounting of UK’s water resources. The main objective of this study is to explore the potential to combine optical and gravity data from EOS with meteorological data, together with innovative in situ sensors, hydrological modelling and crowd sourcing technologies, and the advanced visualization of the information through situation awareness platforms and decision support tools, in order to better monitor, forecast and control the quality and availability of water. Not only the resulting services will be useful for the water industry in UK, but also they are expected to be cost-effective, sustainable, and exportable to broader user groups such as farming industry, environment board, as well as other countries where water issues can be tackled with the same tools.
FutureWater is mainly involved in the following tools and services to be explored during this project:
Airborne Sensors (UAVs):
- Land use classification/elevation maps
- River water quality
- Leakage detection and prevention
- Flood monitoring
- Water availability forecast