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.

La Sierra Nevada de Santa Marta, declarada Reserva de la Biosfera por la UNESCO, es un complejo montañoso aislado de aproximadamente 17.000 km², apartado de la cadena de los Andes que atraviesa Colombia. La Sierra Nevada tiene el pico costero más alto del mundo (5.775 m sobre el nivel del mar) a solo 42 kilómetros de la costa del Caribe. La Sierra Nevada es la fuente de 36 cuencas hidrográficas, lo que la convierte en la principal «fábrica de agua» regional que abastece a 1.5 millones de habitantes, así como vastas áreas agrícolas en las llanuras circundantes utilizadas principalmente para el cultivo de banano y palma aceitera. Los principales problemas por resolver en estas cuencas son: i) Disminución de la disponibilidad de agua para riego, ii) Disminución de la disponibilidad y calidad del agua para consumo humano, iii) Aumento de la salinización de aguas subterráneas y suelos, iv) Aumento de la incidencia de inundaciones.

Este proyecto es un estudio de factibilidad sobre la adopción de técnicas de riego más eficientes por parte de los productores de palma aceitera en la cuenca del río Sevilla (713 km²), una de las cuencas más relevantes en la Sierra Nevada. El objetivo general es identificar el entorno local a nivel de cuenca hidrográfica, los factores limitantes y las intervenciones adecuadas en fincas de palma aceitera para mejorar el uso del agua. Se desarrolló una fase de preparación e implementación que incluyó una evaluación del clima, la disponibilidad hídrica, la amenaza de sequía, las características del suelo, el uso de la tierra y la topografía. Se caracterizaron las variedades de palma aceitera, y las prácticas de campo (por ejemplo, manejo de nutrientes y prácticas de riego), y se determinaron las necesidades de agua de los cultivos. Además, se evaluaron los costos y beneficios asociados a la implementación de tecnologías de riego eficientes como ferti-riego y métodos de cosecha de agua. Se evaluaron ubicaciones potenciales, riesgos y oportunidades para la captación de agua con la idea de almacenar agua en la época lluvioso para poder utilizar el recurso de manera eficiente en la época seca. Se utilizó una variedad de conjuntos de datos SIG y satelitales (por ejemplo, CHIRPS, MODIS-ET, MODIS-NDVI, HiHydroSoil) para evaluar las condiciones ambientales, y los socios colombianos Cenipalma y Solidaridad proporcionaron datos e información local para generar una evaluación integral a nivel de cuenca y de finca. La expectativa es que productores de palma aceitera puedan adoptar técnicas de ferti-riego y cosecha de agua para reducir el déficit hídrico y pérdida de fertilizantes para lograr una producción ambientalmente más sostenible.

The Paris Agreement requests each country to outline and communicate their post-2020 climate actions, known as their NDCs. These embody efforts by each country to reduce national emissions and adapt to the impacts of climate change. As ratifying parties, Armenia, Georgia and Uzbekistan must therefore outline how they intend to implement their NDCs and provide information on what the focus of this spending will be. To support this effort, the Asian Development Bank (ADB) is implementing a knowledge and support technical assistance cluster which will help enhance capacities of developing member countries (DMCs) in meeting their climate objectives by assisting in refining and translating nationally determined contributions (NDCs) into climate investment plans.

In this work package, ADB aims to support Georgia, Armenia, and Uzbekistan with the implementation of their NDCs through developing urban climate assessments (UCAs) and mainstreaming low carbon and climate resilience measures into urban planning processes. FutureWater contributed to this effort by supporting knowledge creation in relation to climate change and adaptation which will help each country to make more informed climate investment decisions.This was accomplished by conducting analysis of downscaled climate model ensembles for different climate change scenarios and synthesising data related to urban climate risk.

Climate change trend assessments were conducted using the NASA-NEX downscaled climate model ensemble combined with ERA-5 climate reanalysis products. To determine climate risk at the urban level, a number of openly available datasets were analysed and compiled using a spatial aggregation approach for 16 cities in the area. Results were presented as user-friendly climate risk profiles at the national and urban scales, allowing for insights into climate trends and risks over the coming century. These will be presented to non-expert decision makers to help support Armenia, Georgia and Uzbekistan develop targeted and informed NDCs.

The Asian Development Bank supports Tajikistan in achieving increased climate resilience and food security through investments in modernization of Irrigation and Drainage (I&D) projects. A Technical Assistance is preparing modernization projects for two I&D systems in the Lower Vaksh river basin in Tajikistan. In line with this, the TA will prepare a holistic feasibility study and project design for the system (38,000 ha), as well as advanced designs and bidding documents for selected works.

FutureWater is part of the team of international experts, working together with the local consultant on the climate risk and adaptation assessment that accompanies the feasibility projects. For this purpose, past climate trends will be analyzed, climate model projections processed, and a climate impact model will be used to assess how the project performs under a wide range of future conditions, to assess the robustness of the proposed I&D investments, and identify possible climate adaptation measures.

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 project should increase agricultural water use productivity in the selected agricultural districts in Uzbekistan through a threefold approach: (i) climate resilient and modernized I&D infrastructure to improve measurement, control and conveyance within existing systems; (ii) enhanced and reliable onfarm water management including capacity building of water consumers’ associations (WCAs), physical improvements for land and water management at the farm level and application of high level technologies for increased water productivity; and (iii) policy and institutional strengthening for sustainable water resources management. This will include strategic support to the Ministry of Water Resources (MWR) and its provincial, basin and district agencies.

The project supports the Strategy of Actions on Further Development of Uzbekistan (2017), which includes: (i) introduction of water saving technologies and measures to mitigate the negative impact of climate change and drying of the Aral Sea; (ii) further improvement of irrigated lands and reclamation and irrigation facilities; and (iii) modernization of agriculture by educating areas of cotton and cereal crops to expand horticulture production.

FutureWater focuses on the climate risk and adaptation assessment that accompanies the feasibility projects, and will analyze climate trends, climate model projections, climate impacts on the projects and assess adaptation options.

Watch the video below to learn more about the management of Climate Adaptive Water Resources in the Aral Sea Basin in Uzbekistan (source: ADB)

The Inle Lake in Myanmar is renowned for a number of traditional cultural and livelihood practices, which have made it one of the main attractions for Myanmar’s booming tourism industry. The lake is, however, suffering environmental degradation from the combined effects of unsustainable resource use, increasing population pressures, climate variability and rapid tourism development. UNDP is supporting the establishment of ILMA, which will have the mandate to manage conservation activities in the Inle Lake protected area.

Under this project, a set of maps will be developed and delivered to the ILMA geodatabase. Different methods, including satellite remote sensing and GIS, will be integrated to complete an updated boundary demarcation of the protected area, based on the Inle Lake watershed boundaries and recent developments in land use. Key ecosystem services of Inle Lake region will be mapped, which will inform an updated zoning (core zone, buffer zone, transition zone) of Inle Lake protected area. Workshops and bilateral meetings are organized to consult with the government stakeholders at several steps during the project, and a training workshop on ecosystem services mapping will be organized at the end of the project.

Myanmar is a country with huge water and agriculture-related challenges. However, ground data on e.g. river flows, rainfall and crop growth are only very sparsely available. This training supported by Nuffic aimed to build capacity across the water sector in Myanmar in overcoming these limitations by using Google Earth Engine, a state-of-the art tool for accessing and processing a wealth of geographical datasets. Participants from academia, higher education, and govenment agencies, attended two training sessions hosted by YTU (the main requesting organization) and implemented by FutureWater and HKV. During the intermediate period, remote support was offered to the participants via Skype, email and the dedicated Facebook page. Results of the individual assignments, which were formulated by the participants based on their personal objectives, were presented in a final symposium.

Higher educational staff was trained to achieve sustainable impact by implementing Google Earth Engine in their curricula and train a new generation of modern and well-equipped water professionals. Public sector representatives participated to obtain skills that can be directly and sustainably implemented in their respective organizations, to benefit effective and equitable water management.

El proyecto Grupo Operativo ECOPRADERAS financiado por EIP-AGRI , tiene como objetivo general mejorar el manejo y la gestión de las praderas mediante: (1) la transferencia e implementación de tecnologías innovadoras, (2) la identificación y fortalecimiento de buenas prácticas culturales, y (3) la difusión de la información y los resultados más relevantes entre los usuarios finales. FutureWater asiste a ECOPRADERAS en lo referido al primer apartado, mediante el encargo específico de desarrollar una herramienta para el seguimiento operacional del estado de las praderas del Valle del Alagón mediante el uso combinado de índices espacio-temporales de satélite.

La metodología empleada por FutureWater utiliza tecnologías de procesamiento masivo de datos en la nube (Google Earth Engine) para calcular un índice cualitativo de estado de la superficie que combina valores de anomalía espacial y temporal del indice de verdor (NDVI). Este indice cualitativo de estado permite categorizar el territorio en diferentes clases y detectar trayectorias o prácticas de manejo pascícola que suponen un riesgo para la sostenibilidad ambiental del sistema productivo y que necesitarían de especial atención.

Las tareas del proyecto incluyen la definición del esquema metodológico, el diseño e implementación de una plataforma web-mapping, y la calibración-validación de los resultados mediante comparación con datos de campo obtenidos en fincas piloto y proporcionados por los socios del proyecto.

Monitor Ecopraderas implementado en el Valle del Alagón (España)

The detection of on-site farm reservoirs and ponds in large areas is a complex task that can be addressed through the combination of visual inspection of orthophotos and the application of automatic pixel classification algorithms.

This analysis applied a general workflow to detect and quantify the area and density of on-farm reservoirs and water bodies in three representative Mediterranean irrigated oases in Sicily-Italy, Northern of Morocco, and Israel. For each area of analysis, the most recent orthophotos available were collected from Google Earth, and the ilastik algorithms were implemented for the pixel classification (Random Forest -RF-) and semantic-segmentation. The RF classifier, which is previously applied to a set of filtered imagery and iteratively trained, provides probability maps of different classes that are finally used for quantitative analysis, or the retrieval of a segmentation-categorical (water vs non-water) maps.