Harshit Rajan is the GIS Specialist in the SAS program at CIMMYT. His role revolves around geospatial activities, primarily centered around his roles within CSISA and SIS. Within the confines of CIMMYT, his professional pursuits are firmly directed toward two critical areas: Drainage class mapping and Digital Soil Mapping, both of which are augmented by cutting-edge machine-learning techniques.
Harish Gandhi is a Breeding Lead for Dryland Legumes and Cereals in CIMMYT’s Genetic Resources program in Kenya. He is a transformative plant breeding and genetics professional, with more than 15 years experience of driving genetic gains, building effective teams, and pioneering innovative research and development.
Carlos Alfredo Robles Zazueta is a Postdoctoral Fellow – Wheat Physiology in the Global Wheat Program at CIMMYT.
His research interests are focused in understanding the physiological basis of yield improvement by studying physiological traits such as photosynthesis, stomatal conductance, biomass accumulation, resource use efficiency, all of this using conventional and high-throughput phenotyping methods.
Lokesh Chaudhary is an agronomist with expertise in seed physiology, crop modelling, precision agriculture and GIS GNSS. He is currently learning about drone piloting, data collection and processing.
At CIMMYT, Chaudhary works on resilient climate agriculture, under which technology transfer is done. Expertise in agronomy, seed and machinery is required and used extensively. He supports in the execution of farmers participatory and on-station demonstrations/research trials on climate-resilient agricultural practices, monitors day-to-day field activities (irrigation, fertilizer, herbicide, insecticide, etc.) and conducts data collection of the farmers participatory/research trials.
Shubham Bhagat is currently working on the Climate Resilience Agriculture program and has expertise in agriculture mechanization and equipment, remote sensing, drone usage and farmer welfare programs, and research on varieties development.
Ramiro Ortega Landa is a rural finance specialist with CIMMYT’s Sustainable Agrifood Systems (SAS) program in Mexico. He provides strategic advice and implementation support to agri-value chains development, articulated to climate goals, and delivers results to CIMMYTâs Global South partners for increasing sustainable, inclusive, and resilient investments to comply with the Paris Agreement.
Landa also develops, implements and manages climate-finance related projects and initiatives that entail innovative financial approaches which harness the power of disruptive technologies and business models, as well as boosting the potential of partnership structures to bring together CIMMYT and the private sector to improving the contribution of climate finance to low emissions and resilient agri-value chain development.
He identifies and supports new and existing partnerships related to agri-value chain development and climate-finance opportunities and initiatives, and provides strategic insights on the latest developments in climate finance, covering private sector and financial actors.
K.M. Zasim Uddin is an agricultural development officer with CIMMYT’s Sustainable Agrifood Systems (SAS) program in Bangladesh. He has a masters in agronomy from Rajshahi University
He is part of projects including the Cereal Systems Initiative for South Asia (CSISA), Fall Armyworm R4D and Management (FAW), Big data analytics for climate-smart agricultural practices in South Asia (Big DataÂČ CSA), and Climate Services for Resilient Development in South Asia (CSRD). His main responsibilities are research and development on agricultural mechanization for the CSISA Mechanization and Extension Activity (CSISA-MEA). He has participated in versatile training, workshops and conference programs across Asia.
Uddin has worked in different national and international non-government organizations and companies for more than 13 years, including in research and development at Syngenta Bangladesh Limited and on the Borga Chasi Unnayan Program at BRAC. He also worked as an agriculture officer under the Char Livelihood Program, funded by the United Kingdom Department for International Development.
Mustafa Kamal is a GIS and remote sensing analyst in CIMMYT, leading the GIS, remote sensing and data team in Bangladesh as part of the Sustainable Agrifood Systems (SAS) program’s Innovation Sciences in Agroecosystems and Food Systems theme across Asia.
Kamalâs core expertise is in earth observation and geospatial data science, scientific and cloud computing, webGIS, Unmanned Aerial Systems (UAS), advance landcover-landuse classification, and tool development. He contributes to research and innovation of irrigation and agro-meteorological advisory, crop identification and yield prediction, disaster and crop monitoring, landscape diversity, and climate analytics. He has published many peer-reviewed papers, reports, and training manuals, and provided teaching/training.
Kamalâs interdisciplinary background in urban and rural planning and disaster management helps him to integrate and lead an interdisciplinary team to provide solutions for sustainable agrifood systems.
Asif Al Faisal is a data analyst with CIMMYT in Bangladesh. He is an expert in artificial intelligence (AI), machine learning modeling, graph representation learning, algorithms, agro-geospatial analysis and data visualization.
Sieg Snapp is the director of the Sustainable Agrifood Systems program at CIMMYT, which brings together global agricultural economics, systems analysis on agrifood innovations and agricultural systems for development in Africa, Asia and Latin America.
As a Professor of Soils and Cropping Systems Ecology at Michigan State University and Associate Director of the Center for Global Change and Earth Observations, she led research on sustainable farming, particularly for cereal-based, rainfed systems in Africa and North America.
Snapp first partnered with CIMMYT in 1993, when she developed the “mother and baby” trial design. This go-to tool for participatory research has developed farmer-approved technologies in 30 countries.
Snapp has partnered with local and international scientists to tackle sustainable development goals, improve livelihoods and farm sustainably. Her two hundred publications and text books address co-learning, ecological intensification and open data to generate relevant science.
Gustavo Teixeira is an Automation and Mechanization Lead with CIMMYT’s Excellence in Breeding Platform.
As a Breeding Operations and Phenotyping module leader, he provides evaluation of breeding program operations according to continuous improvement and operational excellence methodologies and lead initiatives to improve CGIAR and National Agricultural Research Systems (NARS) breeding operations capacities.
Teixeira is an expert in agriculture engineering, processes, mechanization and automatization. He has over 15 years of experience in the private sector, including as Automation Manager for R&D in Latin America at Syngenta.
Scientists at the International Maize and Wheat Improvement Center (CIMMYT) have been harnessing the power of drones and other remote sensing tools to accelerate crop improvement, monitor harmful crop pests and diseases, and automate the detection of land boundaries for farmers.
A crucial step in crop improvement is phenotyping, which traditionally involves breeders walking through plots and visually assessing each plant for desired traits. However, ground-based measurements can be time-consuming and labor-intensive.
This is where remote sensing comes in. By analyzing imagery taken using tools like drones, scientists can quickly and accurately assess small crop plots from large trials, making crop improvement more scalable and cost-effective. These plant traits assessed at plot trials can also be scaled out to farmers’ fields using satellite imagery data and integrated into decision support systems for scientists, farmers and decision-makers.
Here are some of the latest developments from our team of remote sensing experts.
Measuring plant height with high-powered drones
A recent study, published in Frontiers in Plant Science validated the use of drones to estimate the plant height of wheat crops at different growth stages.
The research team, which included scientists from CIMMYT, the Federal University of Viçosa and KWS Momont Recherche, measured and compared wheat crops at four growth stages using ground-based measurements and drone-based estimates.
The team found that plant height estimates from drones were similar in accuracy to measurements made from the ground. They also found that by using drones with real-time kinematic (RTK) systems onboard, users could eliminate the need for ground control points, increasing the dronesâ mapping capability.
Recent work on maize has shown that drone-based plant height assessment is also accurate enough to be used in maize improvement and results are expected to be published next year.
Advancing assessment of pests and diseases
CIMMYT scientists and their research partners have advanced the assessment of Tar Spot Complex â a major maize disease found in Central and South America â and Maize Streak Virus (MSV) disease, found in sub-Saharan Africa, using drone-based imaging approach. By analyzing drone imagery, scientists can make more objective disease severity assessments and accelerate the development of improved, disease-resistant maize varieties. Digital imaging has also shown great potential for evaluating damage to maize cobs by fall armyworm.
Scientists have had similar success with other common foliar wheat diseases, Septoria and Spot Blotch with remote sensing experiments undertaken at experimental stations across Mexico. The results of these experiments will be published later this year. Meanwhile, in collaboration with the Federal University of Technology, based in Parana, Brazil, CIMMYT scientists have been testing deep learning algorithms â computer algorithms that adjust to, or âlearnâ from new data and perform better over time â to automate the assessment of leaf disease severity. While still in the experimental stages, the technology is showing promising results so far.
Improving forecasts for crop disease early warning systems
CIMMYT scientists, in collaboration with Université catholique de Louvain (UCLouvain), Cambridge University and the Ethiopian Institute of Agricultural Research (EIAR), are currently exploring remote sensing solutions to improve forecast models used in early warning systems for wheat rusts. Wheat rusts are fungal diseases that can destroy healthy wheat plants in just a few weeks, causing devastating losses to farmers.
Early detection is crucial to combatting disease epidemics and CIMMYT researchers and partners have been working to develop a world-leading wheat rust forecasting service for a national early warning system in Ethiopia. The forecasting service predicts the potential occurrence of the airborne disease and the environmental suitability for the disease, however the susceptibility of the host plant to the disease is currently not provided.
CIMMYT remote sensing experts are now testing the use of drones and high-resolution satellite imagery to detect wheat rusts and monitor the progression of the disease in both controlled field trial experiments and in farmersâ fields. The researchers have collaborated with the expert remote sensing lab at UCLouvain, Belgium, to explore the capability of using European Space Agency satellite data for mapping crop type distributions in Ethiopia. The results will be also published later this year.
Delivering expert irrigation and sowing advice to farmers phones
Through an initiative funded by the UK Space Agency, CIMMYT scientists and partners have integrated crop models with satellite and in-situ field data to deliver valuable irrigation scheduling information and optimum sowing dates direct to farmers in northern Mexico through a smartphone app called COMPASS â already available to iOS and Android systems. The app also allows farmers to record their own crop management activities and check their fields with weekly NDVI images.
The project has now ended, with the team delivering a webinar to farmers last October to demonstrate the app and its features. Another webinar is planned for October 2021, aiming to engage wheat and maize farmers based in the Yaqui Valley in Mexico.
Detecting field boundaries using high-resolution satellite imagery
In Bangladesh, CIMMYT scientists have collaborated with the University of Buffalo, USA, to explore how high-resolution satellite imagery can be used to automatically create field boundaries.
Many low and middle-income countries around the world donât have an official land administration or cadastre system. This makes it difficult for farmers to obtain affordable credit to buy farm supplies because they have no land titles to use as collateral. Another issue is that without knowing the exact size of their fields, farmers may not be applying to the right amount of fertilizer to their land.
Using state of the art machine learning algorithms, researchers from CIMMYT and the University of Buffalo were able to detect the boundaries of agricultural fields based on high-resolution satellite images. The study, published last year, was conducted in the delta region of Bangladesh where the average field size is only about 0.1 hectare.
Developing climate-resilient wheat
CIMMYTâs wheat physiology team has been evaluating, validating and implementing remote sensing platforms for high-throughput phenotyping of physiological traits ranging from canopy temperature to chlorophyll content (a plantâs greenness) for over a decade. Put simply, high-throughput phenotyping involves phenotyping a large number of genotypes or plots quickly and accurately.
Recently, the team has engaged in the Heat and Drought Wheat Improvement Consortium (HeDWIC) to implement new high-throughput phenotyping approaches that can assist in the identification and evaluation of new adaptive traits in wheat for heat and drought.
The team has also been collaborating with the Accelerating Genetic Gains in Maize and Wheat (AGG) project, providing remote sensing data to improve genomic selection models.
Cover photo: An unmanned aerial vehicle (UAV drone) in flight over CIMMYT’s experimental research station in Ciudad Obregon, Mexico. (Photo: Alfredo Saenz/CIMMYT)
This article by Sakshi Saini and Paresh B Shirsath was originally published on the CCAFS website.Â
Farming has often been quoted as one of the noblest professions, shouldering the responsibility of feeding the world; yet it has been globally identified as one of the most perilous industries associated with a high vulnerability rate. Crop insurance has been established worldwide to provide social protection to farmers and reduce their vulnerability. While the emergence of crop insurance schemes around the world indicates commitment to secure the livelihoods of farmers, they often lack accurate seasonal crop growth monitoring and timely yield loss estimation, making the authentication of crop insurance claims more challenging.
Crop loss assessments are often done via crop cutting experiments (CCEs). However, these can suffer from human error and moral hazard. The experiments also require significant capital and human resources, and need to be carried out simultaneously, in a limited period of time. This often leads to inadequate and delayed claim payment, high premium rates, and poor execution of crop insurance schemes.
With technological advancements and availability, crop growth monitoring and productivity assessment can not only be more accurate and efficient but also less resource-intensive. Readily available data and technology, such as detailed weather data, remote sensing, modeling and big data analytics can be instrumental in further improving crop insurance mechanisms. The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) has developed a Crop-loss Assessment Monitor (CAM) tool as an integrated solution that uses technologies to improve loss assessment and make crop insurance more efficient.
The Crop-loss Assessment Monitor (CAM) tool
The CAM tool integrates multiple input data and methods for crop loss assessment at multiple times in the season. It uses different models for loss assessment depending on the time or stage in the season. To ensure user-friendliness, the tool was developed with a simple, easy-to-use interface and produces outputs customized for policy and risk management agencies. It uses freely available R libraries and does not require specific software installations and high-power processing engines, which in general are a prerequisite to process large gridded satellite data.
CAM provides a form-based user-interface to carry out the analysis. The user can log in and undertake analysis using multiple methods for a specified region and time. The tool allows users to choose between area-based yield insurance and weather-based index insurance. For insurance analysis, scheme details like sum insured and calamity years can be specified for calculation of threshold yields, premiums and claims.
CAM also includes tabs that provide âdeviation in the weatherâ and âdeviation in satellite vegetation indicesâ to help monitor crop conditions every fortnight. The tool also allows users to identify the model agreement between the four different methods for loss assessment, which strengthens the confidence levels in loss assessments, and related insurance analytics.
A single integrated framework
The tool combines agro-meteorological statistical analysis, crop simulation modelling and optimization techniques, and employs near real-time monitoring by using publicly available satellite products. It is also equipped to capture yield variability.
Highlighting the importance of this tool Dr. Pramod Aggarwal, lead author of the paper and CCAFS Asia Program Leader, notes that “assimilating relevant technologies into a single integrated framework is a good way to determine crop losses. Its deployment can assist in multi-stage loss assessment and thus provide farmers with immediate relief for sowing failure, prevented sowing and mid-season adversity apart from final crop loss assessment.”
The tool addresses three major challenges faced by existing crop insurance schemes; more efficient weather indices, timely estimate of loss assessment and improved contract design. As the tool readily uses freely available technology and data, it requires less capital and human resource compared to crop cutting experiments for crop loss assessment. This tool offers a robust mechanism that further reduces the chances of human errors, and makes the process more transparent, robust and reliable. Therefore, it enables timely relief for farmers facing challenges such as sowing failure, prevented sowing and mid-season adversity.
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Gerald Blasch is a Crop Disease Geo-Spatial Data Scientist whose work focuses on research for development (R4D) of remote sensing and geospatial solutions for crop disease early warning systems. He holds a PhD in Agricultural Remote Sensing from Technical University Berlin, Germany, and an MSc in Physical Geography from University Regensburg, Germany.
Blasch has 13 years of research and consultancy experience on both international and national projects in the agriculture and development sectors of several countries (e.g. Australia, China, Germany, Mexico, and the UK). As researcher, he developed remote sensing and GIS tools for precision and conservation agriculture, digital soil mapping, and environmental monitoring during his Post-Doc (Newcastle University, UK) and PhD studies (GFZ Potsdam, Germany), and consultancy activities (CIMMYT, Mexico). As a GIS expert (GIZ, Germany; SEMARNAT, Mexico) he built and managed a GIS for waste management, including capacity building and knowledge transfer.
Walter Chivasa is CIMMYT’s maize seed systems coordinator for Africa. He is responsible for co-developing and executing CIMMYT’s maize seed scaling strategies, managing and developing strategic partnerships, and implementing activities to promote the effectiveness and impacts of CIMMYT products in sub-Saharan Africa. This entails driving and documenting the impact of CIMMYT-derived varieties, contributing to the sustainability, profitability, and growth of seed company partners, and ultimately bringing the benefits of improved and affordable maize seed to smallholder farmers, who face wide-ranging constraints in sub-Saharan Africa.
Chivasa supervises scientists working to improve maize seed systems efficiency through the generation of seed production data, assisting partners in the design and implementation of seed road maps, including inbred line maintenance, production of early generation seed of CIMMYT-derived varieties, and extensive on-farm testing through a network of partners in order to accelerate the deployment of improved varieties.