[HN Gopher] Bali rice experiment cuts greenhouse gas emissions a...
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Bali rice experiment cuts greenhouse gas emissions and increases
yields
Author : PaulHoule
Score : 97 points
Date : 2023-08-15 23:39 UTC (1 days ago)
(HTM) web link (news.mongabay.com)
(TXT) w3m dump (news.mongabay.com)
| morley wrote:
| The "how" took some reading to find. Here it is:
|
| > They filled one field with water, as is common in Bali, but
| they drained the other, wetting the soil only when hairline
| cracks were spotted in the earth
| gumby wrote:
| I was told long ago that the water was needed to support the
| stalks (over the last 20K or so years humans have engineered
| edible grasses to have absurdly large seeds).
|
| But I guess that isn't the case.
| lovemenot wrote:
| Definitely not the case. At least here in Japan, right now
| the seeds are large, but the fields are mostly not flooded. I
| believe the reason for flooding the seedlings is to crowd out
| weeds, which unlike rice are unable to get established.
| richdougherty wrote:
| I guess that's why the article says at the end "The primary
| barrier to entry for farmers is a 500,000 rupiah ($33)
| weeding machine."
| ChatGTP wrote:
| This technique has been used in Japan. It's documented in "One
| Straw Revolution" by Masanobu Fukuoka published 1970s.
|
| In the book he also reported increased yields.
|
| Note: It is a wonderful book, nice read.
| experimental123 wrote:
| Kinda interesting that new farming techniques are still being
| discovered. It seems like someone in the past should have tried
| this experiment and kept some fields unflooded but no one ever
| bothered to do the experiment and measure yields.
|
| Something AI might be good at is suggesting new farming
| techniques and processes for increasing yields. There is probably
| enough literature in agricultural sciences with data for various
| experiments that could be used as a training corpus.
| ceejayoz wrote:
| > Something AI might be good at is suggesting new farming
| techniques and processes for increasing yields.
|
| Why do you believe it'd be good at this?
|
| I asked ChatGPT for a novel farming idea and it gave me
| pseudoscientific bullshit.
| https://chat.openai.com/share/4ba0c013-466e-4bea-ad00-c8ba22...
|
| "Bio-Resonance Farming is a cutting-edge approach that
| harnesses the principles of bio-resonance and plant
| communication to enhance crop growth, health, and yield. Bio-
| resonance refers to the idea that living organisms emit unique
| electromagnetic frequencies, and by understanding and
| harmonizing with these frequencies, we can optimize plant
| growth and overall agricultural productivity."
| experimental123 wrote:
| The current models were trained on a corpus that is
| essentially all fiction with no basis in reality. If the
| training corpus has real world data (like experimental
| results from agricultural experiments with crops and planting
| schedules along with their yields) then the neural network
| should uncover some patterns that wouldn't be obvious simply
| because finding correlations in large data sets is a hard
| problem but it is very well suited to analysis by large
| neural networks.
| ceejayoz wrote:
| I mean, you can go try this now; feed some agricultural
| scientific journals into a model. I suspect it's going to
| be substantially harder than you expect.
| experimental123 wrote:
| I agree it is a very easy to do which is why it's
| surprising someone hasn't already tried it. Most of what
| I see are toy projects with LoRA for generative models
| bolted onto existing LLMs for fiction instead of
| scientific applications. These models already work for
| software so I see no obvious obstructions why they
| shouldn't work for agricultural experiments.
| semi-extrinsic wrote:
| Most software dev is repetitive as hell, monkey see
| monkey do within a computer readable language that has
| well defined syntax. LLMs can do fairly well in this
| niche.
|
| Research is by definition not repetitive, the text is
| free form and the data is _never_ formatted in a way that
| makes comparison between different papers straight
| forward.
| agronomicon wrote:
| That's exactly the type of data set that can be analyzed
| by large neural networks. Heterogeneous data with hidden
| and non-obvious statistical correlations which would be
| hard to uncover with classical statistical tools and
| techniques.
| throwbadubadu wrote:
| Not at all convinced that this is true, the contrary. Do
| you have a reference, or something similar that did this
| successful in another field? (No, that's not ChatGPT and
| writing some limited software).
| galactician wrote:
| Facebook's Galactica.
| gamblor956 wrote:
| It's not very easy to do. LLMs aren't capable of
| understanding, they can merely regurgitate what they've
| read based on statistical analysis of what words appear
| to be linked to each other. That doesn't help you when
| you need to do something new; at best an LLM can tell you
| what someone else has already done.
|
| There are computer programs that do the kind of thing
| you're thinking about, for example, for protein structure
| analysis. They're _incredibly complicated_ and generally
| require a lot of processing power.
| PaulHoule wrote:
| How about
|
| https://www.frontiersin.org/articles/10.3389/fpls.2023.11
| 283...
|
| ?
|
| That's a simple application of machine learning
| algorithms you might find in scikit-learn. Here is a
| special issue of another alleged "predatory journal" that
| is full of papers on the subject
|
| https://www.mdpi.com/journal/agronomy/special_issues/E18K
| 759...
| thfuran wrote:
| >These models already work for software
|
| Do they? You're talking the agricultural equivalent of
| something like "devise a new sorting algorithm with sota
| performance on x, y, z", not "write me some crud
| boilerplate".
| agronomicon wrote:
| I just mean finding correlations in data sets that are
| hard to find in other ways. The main idea is that there
| are plenty of data sets on various cultivars and
| experiments for how to increase yields. There are
| probably patterns in the data that would be amenable to
| analysis by neural networks. The article gives an example
| for how scheduled flooding can increase yields and I bet
| there are a lot of low hanging fruits like that to pick.
| This doesn't require discovering anything novel but
| simply surfacing some patterns in the data that is buried
| across several papers and hard to uncover by classical
| meta-analysis and statistical techniques. Neural networks
| are very good for uncovering non-obvious statistical
| correlations which can then be verified by
| experimentation.
|
| After reading the article I'm sure there are plenty of
| low hanging fruits to uncover in yield optimization by
| trying different schedules for flooding and soil
| enrichment with different kinds of fertilizers. A neural
| network doesn't have to understand anything to point out
| useful statistical correlations just like it doesn't have
| to understand code semantics for incomplete code
| fragments to suggest potential completions which are then
| verified by the programmer/compiler/type system.
| PaulHoule wrote:
| People do all kinds of meta-analysis and literature
| reviews today, I am sure somebody is already applying
| A.I. to the document handling for this task but doing a
| quick search it is hard to differentiate it from
| literature reviews on the subject of A.I. in agronomy
| such as
|
| https://www.frontiersin.org/articles/10.3389/fsufs.2022.1
| 053...
|
| It's a big problem that ChatGPT has seduced a large
| number of people into thinking chatbots = AI and those
| people have convinced most other people that it is a
| scam.
|
| I find 77,000 or so articles on "rice" in PubAg
|
| https://search.nal.usda.gov/discovery/search?query=any,co
| nta...
|
| Just like many other areas, agriculture responds to
| knowledge and is a highly competitive international
| business. For instance, rice is cultivated by very
| different methods in Louisiana and Bangladesh and rice
| from either place could make it to your table.
|
| See
|
| https://en.wikipedia.org/wiki/System_of_Rice_Intensificat
| ion
|
| for a method which is heavy on labor input and light on
| fossil fuel input.
| agronomicon wrote:
| > I find 77,000 or so articles on "rice" in PubAg
|
| Analyzing this data set with an LLM would be a very good
| research project.
| PaulHoule wrote:
| Exactly, and not that hard. My RSS reader has ingested
| about 250,000 articles from random sources since the
| beginning of this year and does a cluster analysis of
| about 50,000 of them every day in under two minutes.
| vkou wrote:
| There's no shortage of ideas for improving real-world
| processes. Most of those ideas are bunk, and we're
| constrained by the amount of experiments[1] we are willing
| to run/fund, and the quality of data[1] that those
| experiments can collect, and the reproducibility[1] of
| those experiments.
|
| Having an AI shout random ideas is very easy for software
| people to grok, but isn't going to help. If you want AI to
| assist with this, you'd need to build an 'AI' that can
| _run_ the real-world experiments, and that 's a few orders
| of magnitude harder than feeding a text corpus to an LLM.
|
| 'Thinking' about this problem isn't the hard part, the hard
| part is _doing_ it. Even using an LLM for something like a
| meta-analysis of existing research is unlikely to find many
| profitable avenues of exploration.
|
| [1] Experimental research is incredibly difficult, which is
| a fact that's highly underappreciated by people working in
| abstract and theoretical disciplines.
| grokist wrote:
| [dead]
| tomrod wrote:
| I have my doubts an AI will reliably generate results that
| are scientifically verifiable.
|
| AI interpolates across it's parameter space, but typically
| performs poorly in extrapolation exercises.
| YetAnotherNick wrote:
| You prompted it in exactly the wrong way. It also says:
|
| > It's important to note that the concept of Bio-Resonance
| Farming is speculative
| wheelerof4te wrote:
| ChatGPT is a glorified CTL+V of loosely connected content
| available on the internet.
| ceejayoz wrote:
| Yes, but even a model trained on a bunch of scientific
| papers will lack _understanding_ in the same fashion, until
| there 's some new technological breakthrough.
| agronomicon wrote:
| Understanding is not necessary for uncovering statistical
| correlations.
| ceejayoz wrote:
| Neither is AI.
|
| The parent poster wants it to _suggest new farming
| techniques_ , which is a little more involved than
| plotting a trend line.
| agronomicon wrote:
| AI is simply about finding correlations in large data
| sets. Computers don't understand anything, they just
| shuffle symbols. So training an LLM on agricultural
| research will likely uncover patterns that would not be
| obvious to people and these patterns could point to new
| techniques and processes for increasing yields like
| scheduled flooding (as explained in the article). LLMs
| don't understand code but they consistently can complete
| code fragments which end up being correct more often than
| not. A model for yield optimization doesn't have to
| understand farming to suggest techniques and processes
| for increasing yields just like LLMs do for code
| fragments.
| Fricken wrote:
| There are all kinds of innovations being made in farming, and
| many more valuable practices from history that have been left
| by the wayside. Big Ag is and has only ever been interested in
| the bottom line.
| galactician wrote:
| [dead]
| [deleted]
| Crowberry wrote:
| It's not a new technique as i understood it from the article.
| It was just abandoned by the introduction of fast growing
| hybrid rice. Nonetheless it's very interesting the experiment
| has not been done before, couldn't have been discovered at a
| better time!
|
| "Lansing, an ecological anthropologist, has studied Indonesia's
| rice fields since he arrived in Bali in 1974 to work on his
| Ph.D. His focus was subak, a rice irrigation system managed by
| water temples, which had been in place since the 9th century
| until it was disrupted by the arrival of the Green Revolution
| in the 1960s and 1970s. Like their counterparts across the
| globe, Balinese farmers were encouraged to swap slow-growing
| local varieties for fast-growing hybrid rice, fertilizer and an
| extra harvest."
| Terr_ wrote:
| I think the flooding of the paddies is also related
| controlling weeds and pests.
|
| So the viability of the technique may depend on other
| technology or resources being available, compared to peasant
| farmers of the past.
| myshpa wrote:
| The father of this method is Masanobu Fukuoka - One Straw
| Revolution, aka natural farming.
|
| https://en.wikipedia.org/wiki/Masanobu_Fukuoka
|
| "in 1947 he took up natural farming again with success, using no-
| till farming methods to raise rice and barley ... organic and
| chemical-free rice farming"
|
| https://youtu.be/nzs8iFGNdBo?t=1412
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