Fri, 9 Nov 2018
Karl G. Gutbrod is the CEO of meteoblue, a Swiss company providing weather and environmental planning services for websites, renewable energy and housing industry. We recently spoke to him about the benefits and challenges of integrating data analytics to drive ROI, and his participation at Ag 4.0.
Sure. It started in 1982 with field research work on irrigated rice and other crops in Brazil, moving to on to cotton and maize in Thailand from 1986-1988. After joining then Ciba-Geigy in 1989 as a field development specialist, I became global product development manager in 1991, and global product manager in 1995. Further stations were global crop management in 1998, and global crop strategy management in 2000, through the second merger to become Syngenta.
The next challenge was global biotech business development in 2001, and finally the responsibility to set up a global Agronomic Information Service in 2004 until 2007.
Through my more than 3 decades of experience in Agriculture, I have seen the globalisation of commodity markets, and actively participated in the expansion of new frontiers (e.g. Soybeans in Brazil, cotton in Thailand, Kiwi in Middle East), and the breakthrough of disruptive technologies (Biotech and Precision Ag) in several continents.
meteoblue was founded in 2006 in Switzerland as a weather modelling and forecasting company. I joined in 2007 and we have since built the company into a unique generator and manager of weather data for any place on Earth, with hourly data for all-important variables (temperature, humidity, precipitation, radiation, wind, and many others) seamlessly available since 1984.
All this is available with verified precision measures, high performance extraction and daily forecast updates. Thereby, we have essentially removed the white spots from the global weather map and enabled a range of innovative applications based on this huge dataset.
Well, Switzerland is not really a leader in agriculture – maybe in the combination of agriculture with landscaping, and tourism. Nevertheless, there is a high degree of penetration for precision monitoring systems, such as weather and soil sensors, growth models and crop forecasting.
We do see growing demand for agtech services in major agricultural markets in Europe, North and South America , and this reflects also in the demand for precision weather services.
I am really looking forward to discussing the step 2 in precision agriculture: the decision to act.
Step 1 is the investment decision – into technology and people on the base level, the field selection and the assignment seeds and inputs to each field. Step 3 is the variable rate application to specific field. Between those, there is the enormously important step 2: that of deciding on each field intervention: soil cultivation, planting, fertilisation, crop protection (eventually irrigation) and finally harvesting.
Each intervention has the potential to influence the final yield and quality, and thereby revenue, by 10 to 100%, depending on the type of intervention. Timing and intensity of each intervention are therefore most relevant decisions farmers make along each cropping cycle and getting those right is the strongest handle on ROI. A reliable, easily understandable and instantly available information base is the best base for optimal decisions.
The first challenge is to understand the cropping process very well, so the options, type and timing of interventions are known. The second challenge is to formulate valid intervention options. The third is to put those in the context of each farm (farm location, size, available equipment) and possible economic impact. The fourth challenge is to execute the interventions optimally … and the fifth challenge is to monitor the results of the interventions, so the next steps can be planned and ideally, maintain a learning system.
Focus on a process step, region or channel. Given the complexity and external influences to which a farming system is subject, you must understand the function and significance of your technology in the total process chain.
Short-term, we want to rapidly find as many as possible applications for our unique range of weather information, so we can add value to the weather dependent processes in agriculture and other industries.
Mid-term, we aim at becoming one of the global key players in supplying global weather data into many industries dependent on precision weather information.
Our last project was MLM, our meteoblue Learning Multi-Model: this is an Artificial Intelligence, which has made our 3-day ahead forecast for temperature as good as the previous 1-day ahead forecast. We are now working on applying this technology to precipitation, radiation and wind.
The second project is to integrate the myriad of weather sensor data which are emerging.
The third project is the go-to-market with our meteoblue-cube, a multi-dimensional global weather data repository, that enables users to find all necessary weather data from 1984 to a six-month forecast and further integrate their own data, to make e.g. regional, farm or field specific risk assessments, crop management and other new evaluations.
LinkedIn: meteoblue AG
Join Karl G. Gutbrod and industry experts at Ag 4.0, a workshop aimed at improving the understanding of modern agriculture practices and creating interoperable solutions. Karl will be speaking on the panel ROI in Precision Agriculture and leading the workshop Applying precision weather data in precision agriculture.
Be part of a workshop where farmers will be given the opportunity to tell AgTech companies what they need, rather than the other way around. If you are a farmer or grower – secure your FREE ticket here.