Sustainability and efficiency analysis w.r.t adoption of climate-smart agriculture (CSA) in Pakistan: a group-wise comparison of adopters and conventional farmers

Imran, Muhammad Ali; Ali, Asghar; Culas, Richard; Ashfaq, Muhammad; Baig, Irfan Ahmad; Nasir, Shoaib; Hashmi, Arshad Hussain

doi:10.1007/s11356-021-17181-3

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…At the same time, excessive inorganic inputs have caused a decline in the quality of agricultural products, a decline in land strength, and pollution from agricultural surface sources. Such a reality indicates that there is an urgent need to further liberate labour, improve productivity and conserve resources, and increase farmers’ incomes and modern skills, supported by SA technologies 12 , in order to enhance endogenous rural development dynamics. Farmers, as implementers, have a direct impact on their adoption behaviour.…”

Section: Introductionmentioning

confidence: 99%

Study on the influence mechanism of adoption of smart agriculture technology behavior

Liu

Chen

et al. 2023

Sci Rep

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Smart agricultural (SA) technology has become a technological support for modern agriculture. By exploring the decision-making process and psychological motivation of farmers in adopting SA technology, it is conducive to achieving the popularisation of SA technology and promoting the modernisation of agriculture. Based on microscopic research data, a Structural Equation Model (SEM) is used to analyse the influencing factors and extent of cotton farmers’ adoption of SA technologies, using Deconstructive Theory of Planned Behavior (DTPB) as the analytical framework. This was combined with in-depth interviews to further reveal the motivations and influencing mechanisms of cotton farmers’ adoption of SA technologies. The results show that under the behavioural belief dimension, cotton farmers value the positive effect of perceived usefulness even though the risk of the technology itself has a dampening effect on adoption intentions. Under the normative belief dimension, superior influence influenced the willingness to adopt SA technologies to a greater extent than peer influence. Under the control belief dimension, factors such as self-efficacy and information channels influence willingness to adopt technology and behaviour. In addition, behavioural attitudes, subjective norms, and perceived behavioural control all contribute to cotton farmers’ willingness to adopt SA technologies, and can also influence behaviour directly or indirectly through willingness to adopt. Policy and technology satisfaction positively moderate the transition from willingness to behaviour. Therefore, preferential policies are proposed to reduce the cost of adopting SA technologies; to continuously improve the level of SA technologies; to establish SA technology test plots to provide a reference base; and to increase knowledge training on SA and expand access to information.

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Section: Introductionmentioning

confidence: 99%

Study on the influence mechanism of adoption of smart agriculture technology behavior

Liu

Chen

et al. 2023

Sci Rep

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“…At the same time, excessive inorganic inputs have led to a decline in the quality of agricultural products, a decline in land strength, agricultural surface pollution and ecological degradation. Such a reality indicates that there is an urgent need to further liberate labour, improve productivity and conserve resources, and raise farmers' incomes and modern skills, supported by smart farming technologies 12 , in order to enhance the endogenous development of rural areas. And farmers, as implementers, have a direct in uence on their adoption behaviour.…”

Section: Introductionmentioning

confidence: 99%

Study on the Influence Mechanism of Adoption of Smart Agriculture Technology Behavior

Liu

Chen

2023

Preprint

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Smart agricultural (SA) technology has become an inevitable trend in the modernisation of China's agriculture and a realistic demand for the transformation and upgrading of traditional agriculture. Based on microscopic research data, a structural equation model (SEM) is constructed using deconstructive planning behaviour theory (DPBT) as the analytical framework to empirically analyse the factors and degrees of influence on cotton farmers' adoption of SA technologies. In-depth interviews were then combined to further reveal the mechanisms influencing cotton farmers' adoption of SA technology. The results show that: (1) the risk of the technology itself has a dampening effect on willingness, but cotton farmers value the positive role played by perceived usefulness more. (2) superior influences such as trial field demonstrations and government publicity have a greater impact on willingness to adopt SA technologies than the group effect of peer influence. (3) factors such as self-efficacy and information channels have an impact on willingness and behaviour to adopt. (4) Willingness influences technology adoption behaviour through mediating effects. (5) Policy and technology satisfaction have a positive moderating role in the occurrence of willingness and adoption behaviour.

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“…Operatively, CSA strategies often focus on greenhouse gases (GHGs) reduction, agriculture's adaptation to climate change, resilience enhancement to weather extreme events [4], and ensuring food security [11]. Overall, CSA strategies aim to meet agriculturalrelated communities' needs (food, fiber, and other primary production materials), increase efficiency, and support sustainable development, poverty relief, and food security through science-based actions capable of responding to climate change challenges [4,12].…”

Section: Introductionmentioning

confidence: 99%

A Long Way toward Climate Smart Agriculture: The Importance of Addressing Gender Inequity in the Agricultural Sector of Guatemala

Mosso

Pons

Beza-Beza

2022

Land

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In the context of climate change’s detrimental effects on agricultural production and food security, climate-smart agriculture (CSA) strategies constitute a promising approach to reduce vulnerability and boost adaptation capacity and resilience within farmers. However, CSA strategies should address gender dynamics to reach their full potential. This study analyzed the barriers and opportunities for the implementation of gender-sensitive CSA strategies in rural Guatemala, a low-latitude country with a high gender gap index, through the perceptions of agricultural extensionists. For this purpose, we conducted an online survey among Guatemalan agricultural extensionists who attended a series of Climate Services for Agriculture workshops between May and July 2021 and analyzed the results using a qualitative approach. Results suggest that women in rural Guatemala are frequently excluded from climate information access, agricultural training, and decision-making spaces in which agricultural resource management strategies are defined. We argue that this exclusion represents a barrier to the improvement in adaptation capacity and resilience and that gender inequity should be addressed to implement successful gender-sensitive CSA approaches. Generating gender-sensitive indicators and training extensionists against gender bias could be a starting point, but further research is necessary to understand gender dynamics in rural Guatemala.

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Sustainability and efficiency analysis w.r.t adoption of climate-smart agriculture (CSA) in Pakistan: a group-wise comparison of adopters and conventional farmers

Cited by 6 publications

References 33 publications

Study on the influence mechanism of adoption of smart agriculture technology behavior

Study on the influence mechanism of adoption of smart agriculture technology behavior

Study on the Influence Mechanism of Adoption of Smart Agriculture Technology Behavior

A Long Way toward Climate Smart Agriculture: The Importance of Addressing Gender Inequity in the Agricultural Sector of Guatemala

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