This month, let’s talk about agriculture, crops and all things related to food!
If there’s one thing that a global pandemic has shown us - it’s how interconnected our supply chains are, especially in the food sector. For most people these days, getting groceries means going to a well-stocked market or food cart and getting fruits, vegetables and other standard supplies from there. We seldom go to the field or orchards or farms to get our food directly from the suppliers. And in general, the supply chains are so well oiled that we rarely run into issues about food not being available - as long as you’re able to pay for it!
The pandemic revealed several aspects of our food system - where our favorite foods come from, how crops are grown, how animals are raised, who harvests and processes our food - and how these systems are so closely connected to each other that impacts on any part of the chain have an effect on the availability of food many miles away.
Pre-pandemic, there was already a trend towards automation and integration of data services in agriculture. In fact, in many developed countries, farming equipment such as tractors and combines already had several sensors and were collecting data about management practices such as fertilization and soil conditions (nitrogen, phosphorus and moisture) among others. The biggest challenge with all the data collected on the farm is that the data tend to be siloed. So, data collected about seed spreading on a field using a tractor often can’t be connected easily to weather data from the weather station or irrigation data from the irrigation system. That makes it difficult for farmers to see all that’s happening on the farm easily! And if you add in finances which are usually kept either on paper or in spreadsheets - you can see that there’s a plethora of data that are useful but difficult to integrate at the farm level.
So, integrating different data sources and managing farm operations are among the first problems that startups working at the intersection of data science and agriculture have tackledover the last 5-10 years. Now of course, we have the pandemic - which has impacted the availability of agricultural labor. That means that the trend towards automating farm labor in terms of including robotic weeding and harvesting tools has intensified, with large companies now moving towards using these robots more often and in larger fields. At the same time, we have a shift towards localization of food supply as people faced empty supermarket shelves and the increased possibility of contaminated food. So, there’s been more interest in vertical farms, local farms and building online marketplaces that connect local farmers with interested buyers.
And, we’re seeing impacts from climate change on the food supply - in terms of changing weather conditions with unseasonal rains, heat waves and droughts, increased predation from pests, loss of beneficial pollinators - a whole suite of issues that are negatively impacting crop yields in many parts of the world. All this is driving governments and farmers in many countries to try and figure out what’s happening and how to best adapt to the changes in store for us. And this is especially true in India, China, Brazil, Argentina, Kenya, South Africa and several other developing countries - where data science, automation, blockchain and increased,easy access to information are helping farmers prepare their fields,sow their crops and sell the produce more transparently and efficiently.
The future certainly looks very interesting in terms of how and where data science is being used in different clean tech sectors. As we’re seeing these shifts happen, the skills and knowledge that will be needed by farmers, agronomists and engineers working in these sectors also change and professionals are trying to keep up with the latest technology and methods. And that’s where companies like ours come in - where we help people gain the skills they need and keep up with the latest research and news at the intersection of these sectors. So, if that’s something that you’re interested in -take a look at our free and paid courses here on our courses website.
What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems? Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively - scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !