There’s an interesting discussion going on over at PBForum, an e-mail based forum for plant breeding and related fields managed by GIPB. It started out with a question from a Philippines breeder about how to get climate-ready rice varieties. I was particularly struck by the latest contribution, which basically said that, rather, we should be trying to…
…create climate-change-ready breeding programmes. That is, build in the flexibility to shift relatively quickly to a new climate related breeding objective, once it becomes established in what direction the climate will change and how it will affect crop yield.
What I would add is that such “climate-change-ready breeding programmes” would necessarily include ready access to as wide a range of raw materials as possible, including, crucially, properly evaluated collections of landraces and crop wild relatives conserved in, and readily accessible from, genebanks.
15 Replies to “How to breed for the future”
I’d say that conventional breeding programs, with multi-location and multi-year testing, are “climate-change-ready” in the sense that they select for broad adaptability (at the expense of rejecting varieties that would be better in specific niches with “unfavorable” conditions). And they do not only select for climate change, but also for pest & disease change. Just keep at it, I’d say. But also add selection for extreme weather (drought, rain, flood, wind) ; as farmers living in areas where these events are frequent may be under-served, and because such events might become more common in the future.
I’m not so sure about the multi-location programmes, etc. Is phenotyping where people put their eggs at the moment? Also, half of the world’s farmers still don’t grow modern varieties and climate change is going to affect them, too.
So, perhaps it is time to start to crowd-source phenotyping? Also, take a look at this.
Before we start new breeding programmes to address climate change there are three distinct existing resources we can tap.
The first is the ability of crops to thrive after long-distance introduction. Seventy percent of crops in both Latin America and Africa are introduced from other continents. They are obviously pre-adapted to the vast range of climatic conditions they found (not least by escaping their co-evolved pests and diseases). Crop introduction is cheap and could be an effective first mechanism to try before expensive breeding. It was the first mechanism used in the USA and the Soviet Union and most of plantation agriculture and was exceptionally effective (with a few failures like coffee in Ceylon).
The second resource is the really vast data base from up to 50 years of multi-locational trials carried out as a matter of course by CGIAR institutes. This will show whether landraces and bred samples will flourish or not all in a wide range of agroecologies.
The third resource is climate-matching. This was used by scientists in CIAT 25 years ago (with their FloraMap software). I was lightly associated with this as a client. We were introducing Leucaena from well-documented provenances in Central America. I wanted to know how we could match the agroclimatic conditions of the site of origin with various locations in Colombia and wider to save us the trouble of planting everything everywhere. This turned out to be no problem. Peter Jones and the CIAT agroclimatology unit produced a mapping system that could match origin with a range of destinations.
I suggest that these approaches be used before breeding. For most conditions resulting from global warming there will already be existing (pre-adapted) land races and bred varieties.
For some photoperiod sensitive crops – sorghum springs to mind – simple day-length conversion (rather than breeding) may be enough.
David, or anyone else out there, can you say more about “the really vast data base from up to 50 years of multi-locational trials carried out as a matter of course by CGIAR institutes. “? There is some data (for about 10 years) on the CIMMYT website for wheat and maize. It is a bit difficult to understand and access; but it is something. What about the other crops and centers?
We are putting together some of these vast (well, maybe not so vast) data sets of multi-locational trials. This site is under construction – http://www.africats.org. Coming before the end of this year, common bean, cowpea, sorghum and millet trial data. This effort will be a sort of crowd-sourced phenotyping. At least that is the idea…..Stay tuned.
It used to be that genotyping was relatively expensive compared to phenotyping, especially decades ago when multi-location trials were more common and biotechnology was inaccessible to many. Now the situation is reversed. Genotyping is getting less expensive every day. But phenotyping is expensive now because of the decline of national agricultural research systems and lower funding overall for agricultural research. This will have to change in the coming years.
That’s cool, but if with “phenotyping” you mean “evaluation”, i.e., “growing out varieties in different places/seasons and measuring their performance (yield and other things)”, how is it that this is cheaper then genotyping? Can you elaborate? Even if you do it “on-farm” there are still considerable costs associated with it, I would think. With prices of biotech plummeting, entire genebanks will probably be sequenced soon. And how is that the cost of genotyping is affected by funding for ag. research. All you need is a little money, seed, and DHL to send it to a company that will process the seeds for you.
I was referring to the relative costs. Yes, the cost of genotyping (biotech) is plummeting, meaning it will depend less and less on trends in ag research funding. Phenotyping costs have probably risen in over the last 15 years or so, just because the experiment stations have declined, due to declining investments in national ag research systems (NARS). In CIAT, we used to have a full time person who did nothing but send out seeds and then organize the results when the agronomists at the experiment stations sent them back (e.g. Int’l Bean Yield Adaptation Nursery – IBYAN – from 1975-1995). CG centers did that on core funds in the past. Nowadays grant funding is the norm.
If the breeding program has a robust multi-location trial program, isn’t phenotyping the most expensive part of a breeding program? Especially for public research that depends on NARS (and in places with less commercial interest)? The seed, DHL, the agronomist to conduct the trial, all the infrastructure of the experiment station – the total costs for all involved it is not insubstantial.
I know of a breeding program that is doing great work, but funding constraints meant they had to decide what to cut. They scaled back phenotyping. Because their NARS partners would need too much support. This is especially true when you are evaluating traits other than yield — traits that need special trial management (e.g. drought tolerance) or a high level of phenotyping skill. But, man, with their fancy biotech labs, they sure can genotype!
Yes, you are right, I really misread your comment. I can be terribly dyslectic before having coffee.
OK, then we agree, multi-locational field trials are expensive and difficult. One of the most stunning features of those trials -I believe- is that when they are conducted, many researchers do not bother to actually measure the environmental conditions at the sites during the trial (weather, soil water status). The other problem is assuring that such data accumulate into accessible databases over time, as Dave Wood referred to. Are those IBYAN data available somehow/somewhere?
We are sorting through the IBYAN data now. Should be up within a couple of months…..RE environmental conditions at trials sights, you are absolutely right. It is scary how poor those data are.
Great to hear that you are working on the IBYAN data. The crop research community is so behind in publicly accumulating this type of data available (in a palatable manner). Show them the way…
15 years of maize trial data from around the world….
Glenn, we have seen some of this work presented (I think?) and I think it is very necessary.
In the future, I think much breeding should be crowdsourced to farmers.
Forget about Fisherian statistics, “stability” (without reference to the environmental conditions themselves!) and that kind of nonsense :-).
This is my recipe:
Step 1. Stick two little bags of crop seeds with unique codes on every bottle of Coke in Africa. Add a little explanation on the bottle and also spread the message through local radiostations.
Step 2. Farmers grow the seeds and then send their opinions with their mobile telephones, sending just the code of the winning seeds. Organize a lottery to provide an incentive. Also get the location of every SMS message from the GSM network (or a GPS coordinate — with newer phones).
Step 3. Link the data with environmental data (remote sensing, interpolated wheather station data, soil data). Delete double entries (the codes are unique, and both varieties cannot be best at the same time.) Then separate the signal from the noise using machine learning. Get info on variety performance in space.
Step 4. Based on the outcomes of Step 3, send variety suggestions for seeds to the mobile phones of all farmers who participated (and suggesting where they may buy them).
Step 5. Do the lottery and send out your free boxes of Coke. Take pictures for the newspaper and agro.agro.biodiver.se.
Step 6. Define a new group of varieties and where they should be evaluated based on the outcomes of Step 3.
Step 7. Go to step 1.
A nice idea.
Two related thoughts:
(1) If mobile phones can be equipped with weather sensors (temperature/humidity/pressure) and if the data can be collected and mapped at no coast to the phone owner, this might provide an accurate and cheap weather forecasting system that is complementary to satellite data.
(2) Does Pepsi occupy a different ecological niche in Africa? We need to know the distribution densities of competing players in the African market. Would it be worthwhile to consider bottled water distribution as well, to reach urban farmers and gardeners?
I guess we need more data to answer the question about Coke/Pepsi niche differentiation: