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:

  1. Real Water Savings in Agricultural Systems including potential field interventions
  2. The use of WAPOR to access remotely sensed derived data
  3. 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:

  1. 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
  2. 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 Sierra Nevada de Santa Marta, a UNESCO-declared Biosphere Reserve, is an isolated mountain complex encompassing approximately 17,000 km², set apart from the Andes chain that runs through Colombia. The Sierra Nevada has the world’s highest coastal peak (5,775 m above sea level) just 42 kilometres from the Caribbean coast. The Sierra Nevada is the source of 36 basins, making it the major regional ‘water factory’ supplying 1.5 million inhabitants as well as vast farming areas in the surrounding plains used mainly for the cultivation of banana and oil palm. The main problems to be solved in these basins are: i) Declining availability of water for irrigation, ii) Declining availability and quality of water for human consumption, iii) Increasing salinization of ground water and soils, iv) Increasing incidence of floods.

This is a feasibility study on the adoption of more efficient irrigation techniques by oil palm farmers in the Sevilla basin (713 km²), one of the key basins in the Sierra Nevada. The general objective is to identify the local environment at basin scale, the limiting factors and suitable field interventions in oil palm areas to improve the water use. A preparation and implementation phase was developed including an initial baseline assessment of the basin on climate, water availability, drought hazard, soil characteristics, land use, and topography. The agronomy (e.g. cultivars) and current field practices (e.g. nutrient management and irrigation practices) of the oil palm areas were characterized, and the crop water requirements determined. In addition, costs and benefits associated to the implementation of efficient irrigation technologies such as fertigation and water harvesting were assessed. Potential locations, risks and opportunities for water harvesting were evaluated with the idea to store water in the wet season to be able to use the resource in an efficient way in the dry season. A range of GIS and satellite-based datasets (e.g. CHIRPS, MODIS-ET, MODIS-NDVI, HiHydroSoil) were used to evaluate the environmental conditions, and local data and information was provided by local partners Cenipalma and Solidaridad to generate a comprehensive assessment at basin and field scale. The expectation is that fertigation and water harvesting techniques can be adopted in the Sevilla basin, but also in other basins in the Sierra Nevada de Santa Marta to reduce the environmental impact of oil palm production.

In Angola, more and better-quality data is required to improve crop suitability assessments over large extensions of arable land to ensure sustainable food and income security. For example, environmental data on soil texture, soil water storage capacity, vegetation growth, terrain slopes, rainfall and air temperature are key to develop reliable crop suitability assessments. These datasets are available from state-of-the-art satellite-based products and machine learning observations (de Boer, 2016; Funk et al., 2015; Hengl et al., 2014, 2017). The benefit of these data products is that data can be obtained for any province, municipality, or farm in Angola. On top of that, data can be shown in maps to easily visualize spatial variation and identify the most suitable location and area to grow desired crops. Land-crop suitability maps are obtained by calculating a weighted average of the environmental variables that influence crop growth (e.g. rainfall, air temperature, soil water storage capacity), providing an integrated and complete assessment on where to plant. Also, potential crop yields are determined for desired cropping seasons using the FAO AquaCrop model to provide more information about potential income.

Irrigated agriculture in Angola has been developed in commercial farms using mainly central pivot and drip irrigation systems. The installation of new irrigation systems is foreseen in large extensions of land over 5000 hectares. Irrigated agriculture results in higher crop yields and allows higher incomes to farmers. However, commercial farms must invest in high energy supply to operate irrigation systems with water pumping stations. The challenge for irrigation system operators is to know exactly when and how much to irrigate during the cropping season. If better information about irrigation volumes and intervals are provided a significal reduction in energy costs could be achieved. The objective is to predict irrigation demand volumes during the cropping season and provide a user-friendly decision tool to irrigation operators. To achieve this, weather forecasts, remote sensing, and the SPHY model will be used.

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.

In irrigated agriculture options to save water tend to focus on improved irrigation techniques such as drip and sprinkler irrigation. These irrigation techniques are promoted as legitimate means of increasing water efficiency and “saving water” for other uses (such as domestic use and the environment). However, a growing body of evidence, including a key report by FAO (Perry and Steduto, 2017) shows that in most cases, water “savings” at field scale translate into an increase in water consumption at system and basin scale. Yet despite the growing and irrefutable body of evidence, false “water savings” technologies continue to be promoted, subsidized and implemented as a solution to water scarcity in agriculture.

The goal is to stop false “water savings” technologies to be promoted, subsidized and implemented. To achieve this, it is important to quantify the hydrologic impacts of any new investment or policy in the water sector. Normally, irrigation engineers and planners are trained to look at field scale efficiencies or irrigation system efficiencies at the most. Also, many of the tools used by irrigation engineers are field scale oriented (e.g. FAO AquaCrop model). The serious consequences of these actions are to worsen water scarcity, increase vulnerability to drought, and threaten food security.

There is an urgent need to develop simple and pragmatic tools that can evaluate the impact of field scale crop-water interventions at larger scales (e.g. irrigation systems and basins). Although basin scale hydrological models exist, many of these are either overly complex and unable to be used by practitioners, or not specifically designed for the upscaling from field interventions to basin scale impacts. Moreover, achieving results from the widely-used FAO models such as AquaCrop into a basin-wide impact model is time-consuming, complex and expensive. Therefore, FutureWater is developing a simple but robust tool to enhance usability and reach, transparency, transferability in data input and output. The tool is based on proven concepts of water productivity, water accounting and the appropriate water terminology, as promoted by FAO globally (FAO, 2013). Hence, the water use is separated in consumptive use, non-consumptive use, and change in storage (see Figure).

Separation of water use according to the FAO terminology.

A complete training package is developed which includes a training manual and an inventory of possible field level interventions. The training manual includes the following aspects: 1) introduce and present the real water savings tool, 2) Describe the theory underlying the tool and demonstrating some typical applications, 3) Learn how-to prepare the data required for the tool for your own area of interest, 4) Learn when real water savings occur at system and basin scale with field interventions.

Methodology

  • Development of adaptation benefit-cost framework: The framework was developed in a manner to make it possible to isolate development- and climate-related benefits and costs of individual projects and to assess the sensitivity of adaptation benefits and costs to the uncertainty inherent in regional climate change scenarios.
  • Development of analytical tools and procedures: The project developed general procedures and specific analytical tools for consistently measuring the costs and benefits of adaptation projects in the agriculture sector in Africa. These procedures and tools allow multi- and by-lateral development institutions to evaluate the benefits and costs specifically related to climate adaptation “add-ons” to sustainable development projects.
  • Application of analytical tools and procedures: The project applied these procedures and analytical tools to estimate the benefits and costs of a well-defined adaptation project in the agricultural sector, particularly on the predominant crop in The Gambia: millet.
  • Water-crop model:
    A detailed water-crop model has been setup and applied for a reference period and for future projected climates. Adaptation strategies have been defined and explored with the model developed and an economic analysis have been applied on the results.

Overview of The Gambia. Landsat composite from 1990.

The major steps taken were:

  • collection of base data and information
  • extraction of IPCC projections for The Gambia
  • downscaling of these projections to the local conditions for The Gambia
  • setup of a crop-water model
  • evaluation of the impact of climate change on yields
  • definition of adaptation strategies
  • evaluation of the impact of these adaptation strategies
  • evaluation of the economics of these adaptation strategies

Result and conclusions

For the development and application of the adaptation benefit-cost framework data from two GCMs were used while concentrating on the most common grain crop in The Gambia: millet. The most relevant adaptation strategies were selected: crop variety improvements, fertilizer applications and irrigation. However, the modeling framework as it is setup can be easily applied to other GCMs, SRES scenarios, crops, soils, or adaptation strategies.

From the analysis it is clear that the impact of climate change on millet yields depends highly on the GCM selected. The HADCM3 projections indicate a much drier future, while the ECHAM4 ones indicate somewhat more rainfall in the future. Considering the “no-regret” principle, we decided to explore the adaptation strategies for the HADCM3 projections only.

Model mean anomaly for A2 Maximum Temperature °C.

Emphasize was put on the annual variation, and more specifically on the successive years of low yields. Introduction of irrigation appears to be the most successful adaptation strategy, yields will increase and, moreover, year-to-year variation decreases substantially.

Variation in annual precipitation over the entire country (Based on: CRU dataset).

A rough estimate of the benefits in terms of gross return was carried out by multiplying the yield by the price of millet (about $ 0.15 kg-1). For the irrigation adaptation strategy this means that the gross return per hectare will increase from $170 to $235. As mentioned before, the reduction in year-to-year variation by the adaptation strategies will be even more important and should be analyzed in detail.

Finally, the most promising adaptations has to be implemented and successive studies should look into whether these adaptation strategies can be adopted through market forces, whether the government should impose these by subsidizes or tax regulations, or whether bi-lateral aid should focus on this in an effort to minimize risks of food shortages.

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

There is so far no accepted general methodology for assessing the significance of climate risks relative to other risks to water resources projects that the World Bank Group supports and invests in. The Independent Evaluation Group (IEG) in its 2012 report entitled “”Adapting to Climate Change: Assessing the World Bank Group Experience””, found that “climate models have been more useful for setting context than for informing investment and policy choices” and “they often have relatively low value-added for many of the applications described” and that “although hydropower has a long tradition of dealing with climate variability, the Bank Group lacks guidance on appropriate methods for incorporating climate change considerations into project design and appraisal.”

The book “”Confronting Climate Uncertainty in Water Resources Planning and Project Design: The Decision Tree Framework”” by Casey Brown and Patrick Ray was published in 2015. Since then, the Decision Tree Framework (DTF) has been applied to Bank projects facing diverse situations in six pilots covering hydropower, water supply, and irrigation with funding from the Water Partnership Program (WPP). This effort is continuing in two additional pilots with financing from the Korea Green Growth Trust Fund (KGGTF) targeting the resilience component of water security of flood protection and irrigation in the Nzoia River basin in Kenya and the application of the Hydropower Sector Climate Resilience Guidelines (which in turn are based on the DTF) to the Kabeli-A hydroelectric project in Nepal.

Together with partners, FutureWater applies the following bottom-up methodology DTF to the Nzoia irrigation project in Kenya and the Nepal’s Kabeli-A run-of-river hydroelectric project study. FutureWater´s main tasks are assessing risks using crop modeling and water allocation modeling of the Nzoia case study, and hydrological modeling of the high-mountain region in Nepal.

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.

Figure 1. Mashhad basin in Iran.

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.

Figure 2. Conceptual framework of SMART-WADI.

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.