Evidence map›Paper›PMID 34476697›Full record

ArticleEnvironmental science and pollution research international2022

An impact of climate change and groundwater salinity on shadow price of water, farmers' revenue, and socioeconomic and environmental indicators in district Kohat-Pakistan.

Arshad Ahmad Khan, Sufyan Ullah Khan, Muhammad Abu Sufyan Ali, Tehseen Javed, Aftab Khan, Jianchao Luo

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Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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5 · Who and what money

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6 authors.

Arshad Ahmad Khan *College of Economics and Management, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China.
Sufyan Ullah KhanCollege of International Cooperation, Xian International University-Xian, 710077, Shaanxi, People's Republic of China.
Muhammad Abu Sufyan AliInternational Business School, Shaanxi Normal University, Xi'an, 710119, Shaanxi, People's Republic of China.
Tehseen Javed *College of Water Resources and Architecture Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China.
Aftab KhanCollege of Economics and Management, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China.
Jianchao LuoCollege of Economics and Management, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China. jchluo@nwsuaf.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Globally, agricultural productivity is adversely impacted due to climatic changes as the temperatures rises and precipitation decreases, and especially in Pakistan, which ultimately enhanced groundwater salinity and harmed water quality in the country. However, the impacts of groundwater salinity and climate change on farmers' revenue have not been fully understood in Pakistan. Therefore, the focus of current research is the assessment of shadow price of water, farmers' revenue, and socioeconomic and environmental indicators affected by variations in groundwater salinity, precipitation, and temperature. The estimation of crop yield sensitivity to groundwater salinity, precipitation, and temperature and their prediction for 2030, 2040, and 2050 time periods was accomplished through the technique of General Maximum Entropy and Response-Yield function. Moreover, the assessment of groundwater quality and climate variable impacts on socioeconomic and environmental indicators was obtained through Target Motad-PMP model. In the end, the most suitable climate change scenario in the study area was established by applying a multi-criteria decision-making method. The results revealed that groundwater salinity and temperature expressed a significantly increasing trend with the Z values of 5.82 and 2.15, respectively. While the precipitation depicted a significantly decreasing trend (Z value = -3.37). The negative impact of climatic changes and groundwater salinity was revealed for revenue risk and shadow prices of water. The most negative impact on income risk and shadow prices is during 2050 horizon with a decrease by 11.4 and 19.4% respectively. The environmental index is the most important with a priority of 43.4% compared to the socio-economic indicators. The sub-index water use is also significant in the study area with a priority of 28.1%. A2 is the most appropriate climate scenario conferring to the TOPSIS ranking method. Therefore, the A2 scenario should be taken into account for the policy of adaptation to the climate change wonder in district Kohat.

Indexed as

FarmersGroundwaterAgricultureClimate ChangeEnvironmental IndicatorsHumansPakistanSalinitySocioeconomic FactorsClimate changeEnvironmental indicatorsGroundwater salinityRevenue riskTarget-Motad-PMP model

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.