scholarly journals Smart Breeding for Climate Resilient Agriculture

2020 ◽  
Author(s):  
Harmeet Singh Bakala ◽  
Gurjeet Singh ◽  
Puja Srivastava
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Crop Improvement ◽  
Human Society ◽  
Wild Germplasm ◽  
Human History ◽  
Improvement Program ◽  
Crop Varieties ◽  
Tackle Climate Change ◽  
Growing Population

Human society is at a turning point of its time as climate change is becoming more and more real and inevitable. From rising temperature, which undermines the food production, to melting glaciers, causing disastrous flooding and erosion, the global repercussions of climate change are unprecedented. Plant breeding has always played a pivotal role in human history by revolutionizing agriculture to feed the ever-growing population. It can rescue humankind from imminent threats to agriculture posed by weather fluctuations, rapidly evolving pests and limiting resources. Unlocking the repository of genetic diversity and extensive utilization of wild germplasm invariably is imperative to every crop improvement program. But recent advancements in genomics, high throughput phenomics, sequencing and breeding methodologies along with state-of-the-art genome-editing tools in integration with artificial intelligence open up new doors for accelerated climate-resilient crop improvement. Therefore, holistic smart breeding approaches can be promising way out to tackle climate change and develop better-adapted crop varieties.

Climate change and abiotic stress mechanisms in plants

2019 ◽  
Vol 3 (2) ◽  
pp. 165-181 ◽  
Author(s):  
John N. Ferguson
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Water Availability ◽  
Crop Improvement ◽  
Research Community ◽  
Comprehensive Understanding ◽  
Physiological Mechanisms ◽  
Ecological Fitness ◽  
Crop Varieties ◽  
Growing Population

Abstract Predicted global climatic change will perturb the productivity of our most valuable crops as well as detrimentally impact ecological fitness. The most important aspects of climate change with respect to these effects relate to water availability and heat stress. Over multiple decades, the plant research community has amassed a highly comprehensive understanding of the physiological mechanisms that facilitate the maintenance of productivity in response to drought, flooding, and heat stress. Consequently, the foundations necessary to begin the development of elite crop varieties that are primed for climate change are in place. To meet the food and fuel security concerns of a growing population, it is vital that biotechnological and breeding efforts to harness these mechanisms are accelerated in the coming decade. Despite this, those concerned with crop improvement must approach such efforts with caution and ensure that potentially harnessed mechanisms are viable under the context of a dynamically changing environment.

How to tackle Climate Change using Artificial Intelligence

2021 ◽  
Author(s):  
Rishikesh Bamdale ◽  
Saurabh Shelar ◽  
Varsha Khandekar
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Tackle Climate Change

scholarly journals Genetic variability for grain yield and yield associated traits in transplant Aman rice (Oryza sativa L.)

2015 ◽  
Vol 2 (2) ◽  
pp. 207-213
Author(s):  
Mashtura Begum ◽  
Md Amir Hossain ◽  
Fakir Muhammad Munawar Hossain ◽  
Ahmed Khairul Hasan
Keyword(s):  
Grain Yield ◽  
Oryza Sativa L ◽  
Rice Variety ◽  
Crop Improvement ◽  
Genetic Advance ◽  
Improvement Program ◽  
Rice Varieties ◽  
Spad Value ◽  
Crop Varieties ◽  
Planting Materials

For any crop improvement program, it is imperative to assess the grain yield progress of the existing crop varieties to find the further avenue to out yield the existing superior ones. Therefore, an experiment was conducted at the Agronomy Field Laboratory, Bangladesh Agricultural University, Mymensingh from July to December 2013 to find out the genetic variation for grain yield and their associated traits of transplant Aman rice varieties. The experiment consisted of 11 varieties viz. Bashiraj, Binadhan-7, BR10, BR11, BR22, BR23, BRRIdhan32, BRRIdhan39, BRRIdhan49, BRRIdhan57 and IR64. The high yielding Bangladeshi varieties were selected based on their releasing year with a local and one exotic T. Aman rice varieties. Among the varieties, BR10 produced the highest grain yield (3.83 t ha-1). Binadhan-7 rice variety recorded the highest chlorophyll content (39.93 SPAD value) at 29DAT, (44 SPAD value) at 39 DAT and (47.30 SPAD value) at 49 DAT. The highest phenotypic (1491.81) and genotypic (1147.26) variances and genetic advance (61.19) were obtained from spikelets panicle-1 and this parameter had greater ability to increase yield. Among the traits, the highest heritability was recorded by effective tillers hill-1 (87.91%) which influenced the grain yield. Therefore, it may be concluded that the variety BR10 of transplant Aman rice produced maximum grain yield, spikelets panicle-1, showed high phenotypic and genotypic variances and genetic advance. Bashiraj, BRRIdhan49 and BRRIdhan57 also can be considered as planting materials as their yield performance is close to BR 10. Therefore, the findings of the present study will help the breeders for further yield improvement of rice.Res. Agric., Livest. Fish.2(2): 207-213, August 2015

scholarly journals Historical Thinking and the Human: Introduction

2020 ◽  
Vol 14 (3) ◽  
pp. 285-309
Author(s):  
Marek Tamm ◽  
Zoltán Boldizsár Simon
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Knowledge Economy ◽  
Human Enhancement ◽  
Historical Thinking ◽  
Final Part ◽  
Human History ◽  
Special Issue ◽  
Making Sense ◽  
Human Introduction

Abstract In recent years the age-old question “what is the human?” has acquired a new acuteness and novel dimensions. In introducing the special issue on “Historical Thinking and the Human”, this article argues that there are two main trends behind the contemporary “crisis of human”: ecological transformations (related to human-induced climate change and planetary environmental challenges), and technological ones (including advancements in human enhancement, biotechnology and artificial intelligence). After discussing the respective anthropocenic and technoscientific redefinitions of the human, the paper theorizes three elements in an emerging new historicity of the human: first, the move from a fixed category to a dynamic and indeterminate concept, considering the human as a lifeform in movement; second, the extent to which the human is conceived of in its relational dependence on various non-human agents, organic and non-organic; and third, the reconceptualization of the human not as one but as many, to comprehend that we cannot speak of human individuality in the classical biological sense. In the final part, the article addresses the consequences of the redefinition of the human for historical thinking. It makes the case for the need to elaborate a new notion of history – captured by the phrase “more-than-human history”, and attuned to an emerging planetary regime of historicity in which historical thinking becomes able to affirm multiple temporalities: digital, technoscientific, sociocultural, human, biological and anthropocenic. The article concludes by recognizing the necessity to venture into a new transdisciplinary knowledge economy, appropriate for making sense of the contemporary constellation of the entangled human, technological and natural worlds.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

scholarly journals Poly(lactic acid) (PLA) and polyhydroxyalkanoates (PHAs), green alternatives to petroleum-based plastics: a review

RSC Advances ◽  
2021 ◽  
Vol 11 (28) ◽  
pp. 17151-17196
Author(s):  
Ahmed Z. Naser ◽  
I. Deiab ◽  
Basil M. Darras
Keyword(s):  
Climate Change ◽  
Lactic Acid ◽  
High Price ◽  
Poly Lactic Acid ◽  
Growing Population

The dwindling nature, high price of petroleum, concerns about climate change, as well as the ever-growing population are all urging the plastics industries to adapt sustainable natural biopolymers solutions such as PLA and PHAs.

scholarly journals Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

2021 ◽  
Author(s):  
Mark Cooper ◽  
Kai P. Voss-Fels ◽  
Carlos D. Messina ◽  
Tom Tang ◽  
Graeme L. Hammer
Keyword(s):  
Climate Change ◽  
Crop Improvement ◽  
Target Population ◽  
Crop Productivity ◽  
Climate Change Scenarios ◽  
Global Food Security ◽  
Genotype By Environment ◽  
Improvement Strategies ◽  
Yield Gaps ◽  
On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

scholarly journals Using Artificial Intelligence to Visualize the Impacts of Climate Change

2021 ◽  
Vol 41 (1) ◽  
pp. 8-14
Author(s):  
Alexandra Luccioni ◽  
Victor Schmidt ◽  
Vahe Vardanyan ◽  
Yoshua Bengio ◽  
Theresa-Marie Rhyne
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Impacts Of Climate Change

scholarly journals Evaluating Wild Germplasm Introgression into Autotetraploid Blueberry

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 614
Author(s):  
Diego Cabezas ◽  
Ivone de Bem Oliveira ◽  
Mia Acker ◽  
Paul Lyrene ◽  
Patricio R. Munoz
Keyword(s):  
Fruit Quality ◽  
Crop Improvement ◽  
Southeastern United States ◽  
Sandy Soils ◽  
Raw Material ◽  
Soluble Solids ◽  
Germplasm Resources ◽  
Wild Germplasm ◽  
Pedigree Data ◽  
Vaccinium Elliottii

Wild germplasm can be classified as the raw material essential for crop improvement. Introgression of wild germplasm is normally used in breeding to increase crop quality or resilience to evolving biotic and abiotic threats. Here, we explore the potential of introgressing Vaccinium elliottii into commercial blueberry germplasm. Vaccinium elliottii is a wild diploid blueberry species endemic to the southeastern United States that possesses highly desirable and economically important traits for blueberry breeding such as: short bloom to ripe period, adaptation to upland sandy soils, disease resistance, firmness, and pleasant flavor. To examine the potential of hybridization, we evaluated populations of interspecific hybrids across multiple stages of breeding (i.e., F1, F2, and backcrosses) in two crop seasons. We used our extensive pedigree data to generate breeding values for pre-breeding blueberry hybrid populations. Hybrid performance was evaluated considering fitness (i.e., plant vigor and plant height) in addition to evaluating six fruit-quality and marketable-related traits (i.e., size, firmness, acidity, soluble solids, weight, and yield). Overall, F2 and backcrosses rapidly achieved market thresholds, presenting values not significantly different from commercial blueberry germplasm. Our results confirmed the potential of exploiting the high genetic variability contained in V. elliottii for interspecific hybridization. Additionally, we developed germplasm resources that can be further evaluated and utilized in the breeding process, advancing selections for fruit quality and environmental adaptation.

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