Brought to you by:
Seeking to make agriculture more profitable and food systems more resilient across the globe, ClimateAi, a startup based in the San Francisco Bay area, is applying artificial intelligence techniques to climate and agronomic data.
With precise weather modeling and seed genetic insights, ClimateAi’s machine learning systems can help farmers understand the long-term suitability of specific crops for a given region, forecast pest and disease pressure and identify hybrids and varieties that will perform.
For this episode of the No-Till Farmer podcast, brought to you by Yetter Farm Equipment, Himanshu Gupta, CEO and founder of ClimateAi, joins us to talk about how his company measures heat risk and drought risk and turns those into what he calls the planting risk index, a metric that can help growers make better operational decisions for their farms.
No-Till Farmer's podcast series is brought to you by Yetter Farm Equipment.
Yetter Farm Equipment has been providing farmers with residue management, fertilizer placement, and seedbed preparation solutions since 1930. Today, Yetter equipment is your answer for success in the face of ever-changing production agriculture challenges. Yetter offers a full lineup of planter attachments designed to perform in varying planting conditions, multiple options for precision fertilizer placement, strip-till units, and stalk rollers for your combine. Yetter products maximize your inputs, save you time, and deliver return on your investment. Visit them at yetterco.com.
Full Transcript
Julia Gerlach:
Welcome to the No-Till Farmer podcast brought to you today by Yetter Farm Equipment. I'm Julia Gerlach, Executive Editor for No-Till Farmer. I encourage you to subscribe to the series which is available in iTunes, Google's podcasts, Spotify, SoundCloud, Stitcher Radio, and TuneIn radio. Subscribing will allow you to receive an alert about new episodes when they're released. I'd like to take a moment to thank Yetter Farm Equipment for sponsoring today's episode.
Julia Gerlach:
Yetter Farm Equipment has been providing farmers with solutions since 1930. Today, Yetter is your answer for finding the tools and equipment you need to face today's production agriculture demands. The Yetter line up includes a wide range of planter attachments for different planting conditions, several equipment options for fertilizer replacement, and products that meet harvest time challenges.
Julia Gerlach:
Yetter delivers a return on investment and equipment that meets your needs and maximizes inputs. Visit them at yetterco.com. That's Y-E-T-T-E-R-C-O .com. Seeking to make agriculture more profitable and food systems more resilient across the globe, ClimateAI, a startup based in the San Francisco bay area, is applying artificial intelligence techniques to climate and agronomic data.
Julia Gerlach:
With precise weather modeling and seed genetic insights, ClimateAI's machine learning systems can help farmers understand the long term suitability of specific crops for a given region, forecast pest and disease pressure and identify hybrids and varieties that will perform. For this episode of the No-Till Farmer podcast, brought to you by Yetter Farm Equipment, Himanshu Gupta, CEO, and founder of ClimateAI, joins us to talk about how his company measures heat risk and drought risk, and turns those into what he calls the planting risk index.
Julia Gerlach:
A metric that can help growers make better operational decisions for their farms. Thank you so much for joining me today. And could you just start by introducing yourself and giving me a little bit about your background.
Himanshu Gupta:
My name is Himanshu Gupta. I'm the CEO and co-founder of ClimateAI. We've been running ClimateAI for the last four years. Started out of Toms at Stanford University, with my co-founder Max, who is son up a farmer himself, and comes from Ecuador. Turns out he has spent a lot of his time on farms with his dad selling pawpaws and pineapples to European food company. And then as an AI nerd.
Himanshu Gupta:
And I came from climate background, having spent almost 14 years now, working in the field of climate change, all the way from what was in public and private sector. I was in government of India where I was responsible for modeling emissions for the Paris climate report for India. And then worked with the government of Brazil, Mauritius, US, UK and whatnot. So, I've not done anything in my life but worked in the field of climate change, so, turns out I was a climate and nerd and Max was an AI nerd, and that's how the company ClimateAI got started.
Himanshu Gupta:
And before that, I come from a small village in the north of India myself, where I remember like, if you get two or three hours off drinking water supply in a day, that's your lucky day. And every time they will be a deficit in monsoon rainfall during summers. That's when most of our equi forest would get resource and most of the drinking water supply would come from. So every time if there's a variability or deficit in the monsoon rainfall, either along when the monsoons arrive, or the first rain as we call it, or it can be amount of rainfall during the season, they'll be even less drinking water available.
Himanshu Gupta:
And I remember traveling with my family to fetch water from a nearby river, every third of our day. And that's why climate change is a problem is very close to my heart, and to my home quite literally.
Julia Gerlach:
Tell me a little bit more about ClimateAI. What your goals are and how you're going about that.
Himanshu Gupta:
We started ClimateAI as a climate resilience platform for food and agriculture supply chains. And what does the resilience mean is, we are seeing more and more impacts of climate change, basically risk, like droughts, heat waves, wildfires, and its impact on crop quality and crop yield and crop safety. And this is not just a long term issue. It's a near term issue. Where if you remember the planting season in the Midwest in 2018, where there were massive floods, or right now we are in the middle of a drought in California, or we call them mega drought, I can go on and on about, you point your finger at any part of the globe, or if there's ecosystem there, you'll see that system suffering from both the short term and the long term impacts of climate changes.
Himanshu Gupta:
So what we do is through our platform, we predict the risk of these extreme weather for a particular crop, and it's impact on the yield quality of that crop. So that all the stakeholders in that crops supply chain, can take actionable decisions, reduces risk in the short term as in the long term.
Julia Gerlach:
Okay. So tell me a little bit more about the decreases that we're seeing in yield, quality and safety. Especially that last one. I'm not sure if I understand that.
Himanshu Gupta:
Yeah. You might have heard of a term called aflatoxin. And aflatoxin are the enzymes produced in reaction to drought and heat stress by corn and groundnuts. Especially during the months of May and June. So if you have an overlap of drought risk and heat risk simultaneously, the crops produces enzymes as a response to a possible fungal attack on the crop.
Himanshu Gupta:
These aflatoxin as we call it or mycotoxin or aflatoxin. Once they get into the food system, it's very hard to protect them. So I need to check the exact numbers, but almost one third of the cases of liver cancer globally, are caused by mycotoxin, aflatoxin in the food system.
Himanshu Gupta:
And because typically, if you have a contamination in food system, it can get detected at the farm gate. We even have multiple sampling procedures and whatnot, but then these mycotoxin are contaminants, even in small quantities, which is less than 2 PPM, can be very dangerous in the food system. And they can go undetected in the supply chain.
Himanshu Gupta:
So in fact, there've been cases where corn has also used as a feed stock for cattle. And remanence of these mycotoxin, aflatoxin are also found in the milk supply.
Julia Gerlach:
Okay.
Himanshu Gupta:
Which is why I mentioned like, it's not just a question of decreasing crop yield, but also a question of decreasing crop nutrition as well as a safety of food system.
Julia Gerlach:
Yeah. I have definitely heard of aflatoxins and especially in regard to it affecting the livestock that eat the feed. But I didn't know that there was carryover to humans as well. So, that's a very interesting bit of information. So, I understand that historically climate changes been responsible or at least recently been responsible for some decreases in yield.
Himanshu Gupta:
Yes. So when you read those projections coming from academics that by 2050, globally agriculture yields will degrees by 30% on average. Because of climate change. I think this data hides more than it reviews. And the reason being, 30% on average yield might not look as big, but then on an year by year basis, or a season by season basis, it could be basically a matter of life and debt for a farmer.
Himanshu Gupta:
And we have seen those cases in Madagascar right now. We are seeing those cases in India. If there's, for example, in India, there've been a lot of cases where farmers commit suicides, if there is a dry season, primarily because farmers don't think they'll be able to repay their operating loans that they undertake, hoping that the cotton yield will be high.
Himanshu Gupta:
When that is not the case, they prefer committing suicides rather than live with that, you know what I mean? As I said, this 30% average might seem pretty less with overall speak of things, but on a year by year basis, what we are seeing the [inaudible 00:08:17] is increasing. So if I lose my yields by 40 50%, or I move my crops by 70%, that reduces my resilience or the capacity to bounce back next year.
Himanshu Gupta:
Now the next year is also bad. Then basically I'm looking at either leaving the farming as a profession for good or declaring bankruptcy.
Julia Gerlach:
Oh, very interesting. Okay. Yeah. And I'm curious about the yields. I feel like every time I see yield data for the US, it's like, average corn crops are going up and up and up. And a lot of that I think has to do with genetics. So, is a lot of the yield decrease, that's more of a global problem than specific to the US?
Himanshu Gupta:
I think yield degrees, it's both a systemic issue as well as an educing credit issue. Right? Systemic because we know along the entire temperate belt, there would be some risk, like the yields of corn are going down. On an average. Right? So if I have a very simple model, where I input soil type, genetics, temperature precipitation, the output is yield. I'll see with the future projections of temperature precipitation, my yield will go down on a blanket basis.
Himanshu Gupta:
But then it's a regional issue because, it depends a lot more on how well can a farmer respond to this season to season volatility of climate. So in many regions where you have good seed genetics available, bread for that local micro climate, they will respond a lot better. The yields will be lower there.
Himanshu Gupta:
In many regions where they have good financial infrastructure, where they're getting loans for 4, 5, 6 years to make transition to a more resilient farming, they are seeing less impact on the yields. And third is if they are following some resilient practices on soil health no-till farming, as well as using drip irrigation and whatnot, then they're also seeing less impact. So, it's a systemic issue, but the response to that is a very resent issue as well.
Julia Gerlach:
Okay. I see. So can you give some more examples of some I guess management changes or practices that would be targeted for farmers or ranchers who are facing those kind of climate changes?
Himanshu Gupta:
Yeah. So, the way I think about practices is based on data, number one. Number two is, data inputs, as well as financial infrastructure. So when it comes to your data, it's important to understand what is the risk that your farm is facing. So typically when we used to talk to farmers three or four years go, the kind of response that we especially in California we used to get is like, "I know once in a 10 year would be a bad year for me, and that's a cost of doing business."
Himanshu Gupta:
But now based on our work that we show to them, that you know, for example in California, the number of chill hours available for growing nut crops like pistachios, almonds is decreasing. And it's not like a once in a 10 year event. It's in a permanent state of decline.
Himanshu Gupta:
You need to first understand using data, what is it that you are dealing with? Understand the risk. Number two is understand on the data front, how can you reduce this risk? You can reduce this risk using more early warning systems, which is, if you are seeing a high probability of a drought and a heat wave simultaneously acting together, that might impact your planting dates.
Himanshu Gupta:
Then you should be planting like later in the season, around early in the season, and should be planting early season variety on late season variety. Late season variety [inaudible 00:11:54] early season variety. Right? So, based on the information you get from the data systems, you pick and make decisions that can help you still react to those risks coming up on a season to season basis.
Himanshu Gupta:
The second, this is on operational basis. On a long term basis, based on the data, you need to decide what can you grow on this land anymore. If I'm growing, let's say this variety of corn, should I be moving to a more drought tolerant, heat tolerant variety of corn? Which might not give me results this year. But I know in the next five years, my overall returns will be higher. Because my region is going to see a lot more of drought and heat waves during the time of planting, during time of pollination during time of harvesting. And so have you.
Himanshu Gupta:
So, data is the first thing. Second is I said like inputs. So based on the data, you'll identify whatever risk you can reduce using early warning systems. The rest of the risk has to be reduced using data inputs. The inputs as I've mentioned would be in the form of better irrigation systems, better drainage practices systems in inside the farm as we saw during 2018 floods in the US Midwest, or like optimizing a water use efficiency.
Himanshu Gupta:
If you are in cloud phone system, it's much more practical to use drip irrigation or techniques which helps you optimize your water use efficiency on the farm. And by the way, which is also significant sustainability benefits where you can get paid more by the food companies. For showing better outcomes on sustainability.
Himanshu Gupta:
And third is financial infrastructure. Right? So, making sure that, I think more and more of lenders are coming up with long term financing plans. So, how do we help farmers? I know about Rao has launched a scheme where they're helping farmers invest for a long term on their finance portfolio. Either you want to go organic, you want to follow regenerative practices, or you want to follow other climate resilient practices.
Himanshu Gupta:
So where the return expectation is not two or three years, but it's like the next five years or 10 years. So making sure that you are talking to the bankers about those loans available. So all these three systems together can help a farm become more resilient.
Julia Gerlach:
And I want to back up just a little bit. We've been talking about farming practices and how all this data can impact decisions that farmers are making. But I want to just back up and talk a little bit more about how ClimateAI is actually populating that data. You said that you've been involved in AI for a long time. Just talk about how that works with climate data. Four years is a great length of time that you've been doing this, but that's not long term results yet. So I'm curious how you're tracking results of your artificial intelligence inputs.
Himanshu Gupta:
Good question. So I'll give you one specific example. We directly do own work with farmers, but we work with agribusinesses around farmers who deploy our platform with farmers. So one specific case is a seed company called Pacific Seeds. They are based in Australia, but they are part of the portfolio of the third largest agrochemical company in the world called UPL.
Himanshu Gupta:
So what Australia is seeing is a lot more of drought stress and heat stress during the time of plant. Especially in Northern Queensland and Southern Queensland. And because of which their customers of seed companies are farmers, are having trouble adapting to these stresses based on the existing seat varieties available. And based on the information that they have available.
Himanshu Gupta:
So what our platform does is, I'll talk about how the AI functions here, but it takes in into account the drought risk, and the heat risk, at a time of planting to the time of harvesting. Put's it together into a nice metric that farmers can really understand, which is a planting risk index.
Julia Gerlach:
Okay.
Himanshu Gupta:
So when is it time you should be planting. Should we be planting two weeks from now, four weeks from now? And five weeks from now for both of our probability, is something that farmers can get in a very tangible format on the dashboard. But also they can input their phone numbers. And the alert will go to their phones as well. That, okay, "The next two or three weeks are looking good for planting." Or not looking good for planting.
Himanshu Gupta:
Based on that, the seed company, if the farmers become more resilient, they see better yield outcomes. Because they planted the right windows. The seed company is already seeing some better outcomes for themselves because based on whatever decisions farmers make, they want to ensure that they have the right inventory in the sales channels in Northern Queensland and the Southern Queensland.
Himanshu Gupta:
So based on our platform deployment over last year, the seed company saw a five to 10% increase in sales for their seeds. And it's based on helping the seed company and the customers of the seed company which in this case are farmers understand, when is the right time to plant. Based on the risk coming up for the next one months, two months, and the next three months.
Himanshu Gupta:
Next one, but mind you, no one has a crystal ball in agriculture and the climate. One of the value on which this company was set up was the value of trust. We take it very seriously. In fact, you can look at our website and trust is something that drives our [inaudible 00:16:53] making, but also as to how we work with our customers.
Himanshu Gupta:
So if we think that the models are not good enough to make any external recommendations, we are very upfront with our customers on that front. However, the question is, what is the current way of doing things which is historical averages, or a cool weather forecast. That's it.
Himanshu Gupta:
And how do we compare as compared to those state of the art models that the farmers use or the seed companies use. And what we have found is using historical averages, we could be anywhere from 10% to 150% better. Now, what that means is, if I'm showing that based on my probabilities, there's a 60% probability or 70% probability of a farmer getting high yields, if they were to plant in the next two weeks, that's a good enough decision making framework, versus when I'm saying that it says that 10 to 20% probability. Then that is something which is not usable at all.
Himanshu Gupta:
So if you want to use historical averages, the uncertainty is very high, and which is why farmers are [inaudible 00:17:53] who like sticking to business as usual, the way they make decisions anyway. But if you can improve the reliability of this data to a level that farmers can make actionable decisions.
Himanshu Gupta:
And again, there are some risks still, but if you were to take decisions for the next four or five growing seasons, on an average, they'll see a lot better outcomes for their farms. And then the second part of the question is like, how do we make it possible using AI. Which ties back to the point number one, which I mentioned around, can we make these predictions reliable enough?
Himanshu Gupta:
So, if you look at weather forecast, weather forecast tend to lose their effectiveness after a week or 10 days. That's number one. And number two, they're not tailor made for consumption for a farmer. I don't care about precipitation. I don't care about just temperature. Tell me what is going to be the soil moisture forecast. That is very useful for me.
Himanshu Gupta:
Tell me what kind of seed I can plant. That is very useful for me. Like the actual disease. So, one of the reason why weather forecast don't do well after seven days and 10 days is because, these forecasts are based on the super computer based models run by Noah in the US. European center or Spaces [inaudible 00:18:59] in Europe. And every country has their own weather models that they use.
Himanshu Gupta:
Weather models fail to take into account the impact of oceanic circulations on a particular region beyond two weeks. Is if oceanic circulations that drive the variability in weather beyond two weeks at any particular location. So you might have heard of these two oceanic circulations called El Niño and La Niña.
Himanshu Gupta:
Then there are 10s of them, a combined influenza of these 10 oceanic circulations will drive what is going to be the probability of a heat wave or a drought for this season in this part of the world or not, depending upon how much close to the ocean they are or not.
Himanshu Gupta:
So, what we did was through our machine learning model, the same techniques that is used by self driving car industry, you have thousands of images coming to a car, and the algorithm of the care is detecting the pedestrian in that image, and making probabilistic judgements around whether the pedestrian is going to move left or right.
Himanshu Gupta:
We flipped the program for climate. Where we said that instead of consider the state of arts, weather as an image. So, just like in a normal image, you have RGB or red, green, blue as a fundamental components of a pixel. In the climate image, you'll have temperature of pressure and wind speed, as a fundamental components of that pixel.
Himanshu Gupta:
So, because we are basically mapping the entire world, including oceans, we are able to incorporate the oceanic data into our models, on top of the models supplied by Noah and European center. And the beauty of that is, just like in a normal image you're detecting a pedestrian, in a time series of climate images, you're detecting a development of a heat wave, over in particular location.
Himanshu Gupta:
So that was the innovation that led us with an outcome which was a forecast which was cheaper, faster, and better, than anything out there. This innovation was developed at Stanford University. We filed patterns on that. We have four patterns on the technology. In fact, we also won a grant from [inaudible 00:21:00] and foundation to deploy this technology globally. And this technology is what is powering our reliable forecast and [inaudible 00:21:06] making tools for farmers.
Julia Gerlach:
Wow, well, that is extremely complicated. But it sounds really exciting especially in terms of really being able to forecast for micro climates. If I'm not mistaken, that's what you're saying. I mean, you can really dial this into a very specific area.
Himanshu Gupta:
Yes. We can go at a farm level, at a point level, or we could be at a vision level as well. It's all the factor of how much data is available. Typically the customers we work with, they also share the data from their own weather station networks. If that data is available, then we can exactly pin point at that point level forecast, and [inaudible 00:21:46] making based on that farm.
Julia Gerlach:
So I think I understood you to say that you're working with the companies that are serving the farmers, but that sounds like this platform is available to farmers directly. Are they paying a fee for that platform? And when they're giving you their data from their weather stations, is there a fee structure involved or some sort of business arrangement?
Himanshu Gupta:
So, as I mentioned, we don't directly sell to farmers. We have partnerships with seed companies who in turn are deploying this platform with farmers.
Julia Gerlach:
I see.
Himanshu Gupta:
So one specific example there is the same of that company called Pacific Seeds. In partnership with Pacific Seeds, we have launched a platform called Skip. And that platform is available for farmers in Australia. And it's the same platform that a seed company uses, but that platform has a lot of design making tools on the farm for farmers, in terms of planting risk, harvesting risk, spraying risk, and then soil motion forecast, humidity forecast, delivered at a very farm level. Either to their phones or onto their laptop like that.
Julia Gerlach:
Mm-hmm (affirmative). Okay.
Himanshu Gupta:
So, the seed company, which is in intern, because they have the trusted relationship with the farmers in Australia, they're agronomists. They're also the ones basically training farmers on how to use this information. And we have a revenue share agreement with the seed company.
Julia Gerlach:
We'll get back to the podcast in a moment, but I want to take time once again to thank our sponsor Yetter Farm Equipment for supporting today's episode. Yetter Farm Equipment has been providing farmers with solutions since 1930. Today, Yetter is your answer for finding the tools and equipment you need to face today's production agriculture demands.
Julia Gerlach:
The Yetter lineup includes a wide range of planter attachments for different planting conditions, several equipment options for fertilizer placement, and products that meet harvest time challenges. Yetter delivers a return on investment and equipment that meets your needs and maximizes inputs. Visit them at yetterco.com. That's Y-E-T-T-E-R-C-O .com. And now back to the podcast.
Julia Gerlach:
How does specific farmers practices, for instance, if somebody's doing no-till, if they use cover crops, maybe specific they're using cereal rye and they plant it in the fall, how did those factors play into how your recommendations would play out?
Himanshu Gupta:
Sure. So that's the beauty of machine learning model. If we have historical six or seven years of data available, on not only the historical yields, the seed varieties and the economy practices that these farmers used, model will be able to identify the linkages between extreme weather risk, and the likely yield quality outcomes for the farmers.
Himanshu Gupta:
So if someone is practicing more resilient practices, such as, no-till as you said, or better for health practices, it would show up in the response that the crop has to these in external stresses because of weather. So we developed this tool internally, where taking into account as I said, soil type, location, historical weather, and the outlook for the next season.
Himanshu Gupta:
We can recommend the portfolio of seed varieties that short season, long season that this farmers can plant, so that they receive a guaranteed yield outcome, no matter how the weather turns out to be. Having said that, we also realize the practical [inaudible 00:25:06]. It's all about building trust with the farmers. So we want to work with seed companies, in deploying it over, let's say 5% or 10% of the farm. If it shows the results, then farmers can deploy it over all of their farm.
Himanshu Gupta:
But we just don't want to act arrogant and tell farmers what to do. They know what to do while they live at their farms. We are just there to enable them to equip them with the right data tools to do that.
Julia Gerlach:
And so I guess I'm just thinking from a farmer's point of view, they want to be able to use this tool and start populating data on this platform. Where do they go? How do they find out who's working with you? What's the baseline amount of information that's useful for them to be inputting into your system for that historical data?
Himanshu Gupta:
I would say a very counterintuitive thing, trust your seed advisors, trust your chemical advisor, crop advisors. And typically our method of working is, let's build a trust with a seed company first, so that they try our technology for one season, two seasons. They get to built that trust, and then they feel comfortable taking it to their customers then this gives us growers, that's the best out for us. Is what we prefer as well.
Himanshu Gupta:
So you'll see in Australia now as I mentioned to you, the canola farmers are starting to use our platform with specific seeds because they already have the trust and because seeds company trust us and their agronomists can recommend the same to the farmers. We'll have the same channel of deployment in the US and Europe as well. As I said, always rely on people you trust. As I mentioned, we are very careful about not breaking that trust with the farmers.
Julia Gerlach:
Mm-hmm (affirmative).
Himanshu Gupta:
As they say, trust takes years to build and minutes to destroy.
Julia Gerlach:
True.
Himanshu Gupta:
Especially in a predictive analytics business, if you are not explaining the context of how to best use this predictive technologies, it can then end up harming a particular farmer than helping.
Julia Gerlach:
Of course more data seems like it's always better, but is there certain like a farmer would be expected to input three years of data or something like that to get that baseline established?
Himanshu Gupta:
In the first step we don't require any farmer level data.
Julia Gerlach:
Okay.
Himanshu Gupta:
There are these in house indices that they're [inaudible 00:27:29] making because there will be a built, which is based on farmers to input the Lat Long, latitude and longitude of their farm. And the crop type, let's say the canola, corn, or [inaudible 00:27:38] crops or what have you. And they can start receiving actionable insights from our tool right away.
Himanshu Gupta:
So A, they don't need to input their data in the first season. You see the performance of the platform for the first two season and three seasons and then you can start [inaudible 00:27:54] like, okay, if I input more data, then that might lead to even better outcomes as far the recommendations from this platform are concerned.
Julia Gerlach:
Is there anything else that you would like to talk about?
Himanshu Gupta:
I think there have been a lot of announcements in COP26 by the Biden administration and if I'm not wrong, around $50 billion or more has been allocated for agriculture. I saw that plan, I think it's a very ambitious plan and it's kind of the right time. There are a few things which are missing from that plan, and I define them in three words which is, 3Ds, definition, data and diffusion. The plan talks about encouraging climate smart agriculture in the US and promoting resilient practices.
Himanshu Gupta:
So how do you define resilient practices? What is resilient? Is it aligned with income resilience of the farmers, or is it aligned with yield resilience of the farmers, or what? So there's a lack of clarity on definition. Second is the data. Unless we have a right channel of data ingestion within USDA and vice versa, great communication of data transfer and data monitoring and availability from farmers to [inaudible 00:29:09] and vice versa is very hard to determine what practices are client smart and what practices are leading to resilient outcome or not.
Himanshu Gupta:
And third is diffusion. So there's been so much of money allocated, the plan talks about this money will go to USDA Climate Hubs that will drive the implementation of these decision making tools to the farmers. But then it's not the first time that USDA Climate Hub has been doing that.
Julia Gerlach:
Right.
Himanshu Gupta:
They have a set up exactly for the same reason. They have developed these climate smart tools for the last 10 years and 12 years. They have really bright scientists working in there, but it's worth investigating how much of those tools have actually been used by the farmers. And if not, what are the reasons why those tools were not used and not been successful?
Himanshu Gupta:
So I talked about diffusion process. How do you diffuse the impact of funding all to the farmers, right? Without investigating why it has not been that successful, I think there needs to be a better layout of the plan based on the three things that I identify. Definition, data and diffusing.
Julia Gerlach:
Yeah. And a lot of it hinges on the fact, I think, that most of the plans have always been voluntary, and I think they need to be voluntary. Farmers don't want to be mandated to make changes. And they will do things when it's in their best interests as well as the climate. But as you say, the overall impact is, do you feel like your platform has the ability to bring that all together and address the fact that the policies and the practices that have been implemented have not been that effective?
Himanshu Gupta:
Policy and mandates have their own importance and role to play, but as you said, they cannot mandate and tell any farmer what to do. Right? Our objective on the other side is to create those market driven incentives for the private sector, or the agribusinesses all the way from seed companies, to fertilizer companies, to food processors, so that they invest in the resilience of the farmers.
Julia Gerlach:
Yeah.
Himanshu Gupta:
So when they invest in the resilience of the farmers, of course, the farmer see better outcomes, but also the food companies and the food processors or the seed companies see better top line and bottom line.
Julia Gerlach:
Mm-hmm (affirmative).
Himanshu Gupta:
And to your point Julia, as you said, farmers will do if something is in the best interest and exactly the mechanism we want to create that, if let's say the seed company deploys a platform that I talked about with hundreds of thousands of farmers in Australia mentioning that this platform is going to lead to better outcomes for you in terms of [inaudible 00:31:49], but also allow us to serve you better.
Himanshu Gupta:
And that is exactly our motto. The second point is around no matter whether we are on two degrees or four degrees global warming pathways, as we talk about, climate change is already here, right? And even with 1.2 degrees, our first priority has to be the adaptation. Adaptation of farmers in agriculture, both in developed economies as well as developing economies.
Himanshu Gupta:
And the way it's showing up as I talked about is of course, increase in extreme weather events around agriculture growing reasons globally, but also shifts in the suitability of growing crops. So there are some areas coming up, which are becoming suitable for growing, let's say wine grapes or corn, which were not before. And the existing areas are becoming unsuitable.
Himanshu Gupta:
Our objective is, as the shift is happening, how do we ensure that the farmers are at the benefiting end of the ship? Through market driven incentives. We don't expect governments to mandate anything. All we expect is basically policy and the plans to become enablers over transition.
Julia Gerlach:
Yeah. Okay. And you had mentioned incentives, and you had mentioned something earlier about processors, perhaps rewarding more resilient practices. So is that something that you actually foresee happening? And what other incentives do you foresee companies using?
Himanshu Gupta:
It's already happening. As you know, carbon markets have become the talk of the town because there's good enough demand for carbon credits based on the soil sequester carbon. So from companies like Microsoft, and Unilevers of the world, as well as the players in the value chain. And we're talking about seed companies and food processors, who want to enable farmers to take benefit out of it.
Himanshu Gupta:
But also it helps these companies improve their brand positioning to their customers. So that transition is already happening. What we need to ensure is, this transition is permanent, is measurable, as well as more of this value is transferred to the farmers.
Himanshu Gupta:
I came from climate and energy supply chain to farming supply chain. I was so surprised to see that farming supply chain is the only supply chain where producers, in this case, farmers don't have a pricing power at all.
Julia Gerlach:
Hmm.
Himanshu Gupta:
Like you look at oil and gas supply chains, Saudi Arabia drives can basically manipulate the price of oil globally, they're producing that. In this case farmers are basically at the receiving end of the price. That's one.
Himanshu Gupta:
Two is, they are the only stakeholder in the agriculture supply chain who are least diversified. So as an example, a seed company might be selling to farmers globally. A food processor might be sourcing from six different regions. So even if there's a drought in one region that's even in years there, the food processor is covered. But a farmer if they end up losing their yield for the season, they're only selling to one food company, or one market, right?
Himanshu Gupta:
So they're the most vulnerable and exposed to climate risk and that to me coming from a different sector, was very surprising.
Julia Gerlach:
Thanks to Himanshu Gupta of climate AI for this discussion about leveraging climate intelligence for better No-Till results. To listen to more podcasts about No-Till topics and strategies, please visit no-tillfarmer.com/podcasts.
Julia Gerlach:
Once again, we'd like to thank our sponsor Yetter Farm Equipment for helping to make this No-Till podcast series possible. If you have any feedback on today's episode, please feel free to email me at jgerlach@lessitermedia.com or call me at (262) 777-2404.
Julia Gerlach:
If you haven't done so already, you can subscribe to this podcast on iTunes, Spotify, or Google Podcast to get an alert as soon as future episodes are released. You can also keep up on the latest No-Till farming news by registering online for our No-Till Insider Daily and weekly email updates, Dryland No-Tiller e-newsletter and be sure to follow us on Twitter @NoTillFarmr with farmer spelled F-A-R-M-R and our No-Till Farmer Facebook page. For our entire staff here at No-Till Farmer I'm Julia Gerlach. Thanks for tuning in.
