Question
Asked 21 July 2024

How can artificial intelligence be leveraged to enhance the accuracy and efficiency of climate change impact predictions on agricultural productivity?

This question seeks to explore the role of artificial intelligence in improving the prediction models for climate change impacts on agriculture. It aims to understand how AI technologies, such as machine learning and data analytics, can be utilized to analyze vast amounts of climate data, soil conditions, and crop yields to provide more accurate and timely predictions. The discussion could also cover the potential benefits of these AI-driven predictions for farmers, policymakers, and researchers in developing strategies to mitigate the adverse effects of climate change on agriculture. This topic is crucial for ensuring food security and sustainable agricultural practices in the face of changing climate conditions.

All Answers (3)

Andres Halabi
Andrés Bello University
As in what type of method you would have to use, anything related to Machine Learning and Artificial Intelligence can usually be reduced to three aspects:
(1) Data recollection
(2) Model building and Validation
(3) Model interpretation and insights
Without having much knowledge about what particular crop or problem you are studying, you will have to gather a comprehensive database with experimental data measuring the variable you want to predict (for example total crop recolection, time to maturity, time to harvest...etc) and with measurements or estimations of the variables you expect to be important for your problem (in this case for example, water availability, temperature and CO2 concentration will probably be useful descriptors).
Once you have assembled a good dataset, you use ML to select the best attributes and then to build predictive models on your dataset.
Then you have to read and understand the predictive models, trying to learn insights about your problem from them.
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Mohammed Ahmed
Baghdad University College of Science
Artificial intelligence can be used by measuring changes in temperature, air humidity, lack of soil moisture, and lack of watering of plants, which affects the accuracy and efficiency of predictions.
1 Recommendation
Viera Rattayova
Slovak Hydrometeorological Institute
I used machine learning models for modeling missing data on actual sunshine duration, net radiation on the earth's surface, and evapotranspiration. I tested various approaches, models, and feature selection methods to determine how they affect modeling results. Many countries face challenges with the availability of certain types of measured data, and climatological stations are not available in every locality. In my country, we have enough climatological stations, but only some of them measure actual sunshine duration or radiation. At stations where these measurements are available, the time series often contain missing dates.
The value of actual sunshine duration (or net radiation) is important for calculating reference evapotranspiration (ET0) using the FAO56 P-M methodology. This can also be used (after multiplying by the crop coefficient) as an alternative to actual evapotranspiration. These variables are necessary inputs for many models used in the assessment of aridity, drought, the impact of climate change, and hydrological modeling.
I modeled these variables using other measured climatological data from the stations, remote sensing data, and freely available climatological data. Additionally, as a new approach to data homogenization, I used time series of the same variables (ETc, ET0) from similar stations nearby. The results showed that ML models provide very good predictions in all approaches, with precision significantly better than that of traditionally used models. I see great potential in using machine learning models for all types of tasks related to data modeling for assessing climate change.
1 Recommendation

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Call for Papers | Journal of Artificial Intelligence for Medical Sciences (JAIMS)
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  • Kiuling LaiKiuling Lai
Call for Papers | Journal of Artificial Intelligence for Medical Sciences (JAIMS)
*Publication in this journal is free of charge for Authors in 2024
(Note that this is a promotional offer which applies to all papers submitted before 31 December 2024)
Aims and Scope:
Journal of Artificial Intelligence for Medical Sciences (JAIMS, Online ISSN 2666-1470) is an international peer reviewed journal that covers all aspects of theoretical, methodological and applied artificial intelligence (AI) for medical sciences, healthcare and life sciences.
The Editors welcome original research articles, comprehensive reviews, correspondences and perspectives that provide novel insights into diagnostics, drug development, care processes, treatment personalization with the support of machine/deep learning, data science, natural language processing (NLP), etc.
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Read freely available Journal of Artificial Intelligence for Medical Sciences Editor in Chief: Prof. Zhisheng Huang (Vrije Universiteit Amsterdam (VU), Amsterdam, The Netherlands) Deputy Editors-in-Chief: Prof. Xue Li (The University of Queensland, Brisbane, Australia) Prof. Xingpeng Jiang (Central China Normal University, Wuhan, China) Submit your paper if you would like to be considered for this journal:
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  • Alexander OhnemusAlexander Ohnemus
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WARNING: GENETIC ENGINEERING IS DANGEROUS. 1)This ANTI-racist essay rejects both racialism and racial essentialism, beginning with epistemology, physics, chemistry, biology, and then social sciences, of course with some overlap. Humans have, AT LEAST PARTIAL, free will or they could not fundamentally choose to focus on life, thus all reason would be futile “And if humans lack free will then the reasoning behind anything would not exist”(Ohnemus 2023). Thus self-evident PARTIAL free will debunks biological determinism. Biological determinism, at least in this essay and many other senses, also means scientific materialism. Thus, humans probably have souls, OR AT LEAST hereditarianism is wrong. Science only approximately derives from philosophy because direct derivation would be a non-sequitur. Plus, the relationship between science and philosophy is very complicated. Philosophy speculates more and changes upon scientific discovery. Yet science rests upon philosophical postulates, such as the philosophy of science. 2) As a cohesive thesis elaborates, certain deceased Northwestern Europeans, who NEVER reproduced, either owe several reparations to the lesser privileged racialized populations OR were so enlightened, they deserve to have their traits expanded. Exact replication(both phenotypical and genotypical) not only is probably impossible, but also may excessively reduce diversity. Thus, 3D printing more progressive(traits and genes) and recessive(traits and genes) INexact replicas of those certain, and other, Northwestern Europeans is imperative for civilization and progress. Perhaps following the mass automated reproduction, to mitigate risks, the recessive and progressive privileges may be distributed to the consenting individuals, of racialized populations with lesser privileges. 3) Beginning from first principles does not mean reductionism. On the contrary, boiling down to the most fundamental truths and then reasoning up, while acknowledging and mapping nuance, may be the surest avoidance of both reductionism AND contradictions, if one simultaneously recognizes and charts counterintuitive reality. If diseases can reasonably correlate to genes with a parsimonious relationship then, other traits can too. Potentially one can describe a dead person’s traits to a machine(maybe through photos, recordings, verbally, etc.) then, the machine can at least create one of many possibly corresponding genotypes. Humans, at least typically, only have 46 chromosomes, so certain traits parsimoniously match with certain genes. Even humans with more, or maybe less, than 46 chromosomes probably still have a close enough number for their traits to be parsimoniously matched with corresponding genes. Plus if previous information on the dead subject’s genotype is available(DNA of relatives, lineage DNA, ethnic DNA, populational(maybe racialized) DNA, general human genome, genealogy, etc.) data, both of the particular deceased person, one can either clone that dead subject(having deduced the genotype) or 3D bio-print another human with the same traits the deceased is remembered for, plus silver lining, added genetic diversity. One could 3D bioprint an in-exact replica of the deceased individual through the deduced genotype, out of bioink materials composing DNA, RNA, and proteins(perhaps sugar or other elements that chemically make deoxyribonucleic acid), while a bioreactor reinforces the design, and rheology, of the intended human. If CRISPR can both cure, and accidentally cause, mutations, then it can potentially and intentionally distribute recessive and progressive privileges by both somatically, and germinally, mutating people. CRISPR could even distribute both recessive and progressive privileges, of being enlightened liberal Northwestern Europeans, to 3D bioprinted people before they are printed. 4) Selectively 3D bioprinting human individuals for humanitarian ANTI-racist reparations is key for preventing a phenotypic revolution because recessive ≠ useful to machines. Plus, by 3D bioprinting, in-exact human replicas, of deceased individuals, machines won’t be able to launch a phenotypic revolution, because the automated reproduction will lack the linearity. And the philosophy, of paying ANTI-racist reparations, requires a level of empathy that is uniquely human and not automatable.
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