scholarly journals Climate impacts on the agriculture sector of Pakistan: Risks and amicable solutions

2022 ◽  
pp. 100433
Author(s):  
Areeja Syed ◽  
Taqi Raza ◽  
Talha Tufail Bhatti ◽  
Neal S. Eash
Keyword(s):  
Climate Impacts ◽  
Agriculture Sector

scholarly journals Temporal Aspects in Emission Accounting—Case Study of Agriculture Sector

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 800 ◽  
Author(s):  
Lelde Timma ◽  
Elina Dace ◽  
Marie Trydeman Knudsen
Keyword(s):  
Climate Impacts ◽  
Ghg Emission ◽  
Cycle Model ◽  
Emission Mitigation ◽  
Carbon Cycle Model ◽  
Time Dynamic ◽  
Agriculture Sector ◽  
Response System ◽  
Temporal Aspects

Complex relations link climate change and agriculture. The vast majority of the studies that are looking into the quantification of the climate impacts use the Global Warming Potential (GWP) for a 100-year time horizon (GWP100) as the default metrics. The GWP, including the Bern Carbon Cycle Model (BCCM), was proposed as an alternative method to take into consideration the amount and time of emission, and the fraction of emissions that remained in the atmosphere from previous emission periods. Thus, this study aims to compare two methods for GHG emission accounting from the agriculture sector: the constant GWP100 and the time dynamic GWP100 horizon obtained by using the BCCM to find whether the obtained results will lead to similar or contradicting conclusions. Also, the effect of global temperature potential (GTP) of the studied system is summarized. The results show that the application of the BCCM would facilitate finding more efficient mitigation options for various pollutants and analyze various parts of the climate response system at a specific time in the future (amount of particular pollutants, temperature change potential). Moreover, analyze different solutions for reaching the emission mitigation targets at regional, national, or global levels.

Climate Impacts and Psychology

2014 ◽  
Author(s):  
Bob Perkowitz
Keyword(s):  
Climate Impacts

Climate Impacts on Energy Systems

2011 ◽  
Author(s):  
Jane Ebinger ◽  
Walter Vergara
Keyword(s):  
Energy Systems ◽  
Climate Impacts

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.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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