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Which religion could fix your life??????

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Exploring the Impact of Religion on Personal Well-Being

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Exploring the Impact of Religion on Personal Well-Being

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Can the principles and teachings of a particular religion provide the guidance needed to navigate life's challenges and foster a sense of purpose and fulfillment?

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Gender Inequality in Sports: Challenges Faced by Female Athletes

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Gender Inequality in Sports: Challenges Faced by Female Athletes

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Female athletes continue to face gender inequality, battling unequal funding, representation, and opportunities in sports.

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Gender inequality in sport: The challenges facing female athletes

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Despite the progress made in women’s sport, major challenges remain, particularly regarding equal pay, visibility and training conditions.

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The Impact of Sleep Quality on Academic Performance Among College Students

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The Impact of Sleep Quality on Academic Performance Among College Students

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How does the quality of sleep directly influence the academic success and overall well-being of college students, and what strategies can be implemented to enhance sleep hygiene for improved educational outcomes?

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Abstract

Background

Sleep is an inseparable part of human health and life, which is crucial in learning, practice, as well as physical and mental health. It affects the capacity of individual learning, academic performance, and neural-behavioral functions. This study aimed to determine the relationship between sleep quality and students' academic achievement among students at Kermanshah University of Medical Sciences.

Methods

In this cross-sectional study, 102 medical students from different fields, with maximum variation sampling, completed Pittsburgh Sleep Quality Index (PSQI). For data analysis, SPSS 19 was used through which Pearson correlation test, Spearman test, and t-test were employed.

Results

Based on the quality of sleep questionnaire scores, the results indicated no significant difference between students with high grades and those with low grades. However, there were moderate and sometimes severe sleep disturbances in both groups.

Conclusion

The results showed no significant difference between sleep quality and academic achievement. Nevertheless, longitudinal study should be performed to control for confounding factors.

Keywords: quality of sleep, the academic achievement, students

Background

Sleep is an inseparable part of human health and life, and is pivotal to learning and practice as well as physical and mental health.1 Studies have suggested that insufficient sleep, increased frequency of short-term sleep, and going to sleep late and getting up early affect the learning capacity, academic performance, and neurobehavioral functions.2,3 Previous studies have indicated that the quantity of sleep reported by individuals as delayed or inappropriate sleep, waking up too late, especially at weekends and daytime sleepiness is associated with compromised academic performance in children and adults.2 Some studies have emphasized the relationship between delayed starting time of classes and academic success.4 Reduced overnight sleep or altered sleep patterns has been associated with severe drowsiness and failure in academic success.5 In a study, people who had enough sleep compared to their sleep-deprived individuals used innovative solutions twice as often when confronted with complex mathematical problems.6 The chance of academic failure was as long as one or more than 1 year in students with inadequate sleep compared to those with proper sleep.7 People who sleep less and sleep during the day are more prone to vehicle and work accidents.8 In some studies, sleep efficiency has been considered as essential for recovery, cognitive processing, and memory integration.9 On the other hand, lack of sleep has been associated with emotional instability and impaired concentration.10 In this regard, students are particularly at risk of developing sleep disorders and development of the disorder among them has a negative effect on their academic performance across different grades,11⁻13 However, there is no consensus in this case and not all studies state that sleep disorders yield a negative effect on academic performance. Eliasson (2010) believes that the time it takes to fall asleep and waking up affect academic performance more than duration of sleep does.14 Sweileh and colleagues (2011) also believe that there is no relationship between sleep quality and academic success.15 Similarly, it is claimed there is no relationship between the night sleep before the exam and test scores either.16

In another study, the author believes stress from lack of sleep causes poor school performance.17 On the other hand, in a systematic review, the authors could not establish a cause and effect relationship between sleep quality and academic performance.2 In their meta-analysis study, Dewald and colleagues (2010) emphasized that because of the diversity of the methodology of studies, it is impossible to definitely derive a relationship between sleep quality and academic performance, and thus more longitudinal intervention studies are warranted.1 According to different conclusions in this respect, the researchers decided to determine the relationship between sleep quality and academic performance among students at Kermanshah University of Medical Sciences.

Methods

In this cross-sectional study, through maximum variation sampling, the first three students with highest scores and three last students with lowest scores were selected, and the Pittsburgh Sleep Quality Index (PSQI) was completed for them.

The study population consisted of students of Kermanshah University of Medical Sciences. The samples were also students at each school with the highest GPA (first three high scores) and the lowest GPA (last three lowest scores). The sampling was purposeful sampling with maximum variation. The sample covered a number of disciplines in the third semester and above ( Figures 1 & 2 ). After determining the target students, the questionnaire was given to them and then returned to the researcher after completion.

The data collection instruments were demographic form (including age, gender, place of residence, grade, rank in the class, discipline) and Pittsburgh Sleep Quality Index (PSQI). PSQI is a self-report questionnaire which examines the quality of sleep. It has 18 questions which are classified into seven components: the first component is the subjective sleep quality which is determined with Question 9. The second component is related to delays in falling asleep, where the score is calculated by two questions, the mean score of Question 2 and part of Question 5. The third component deals with sleep duration and is determined by Question 4. The fourth component is related to the efficiency and effectiveness of sleeping in patients. Its score is calculated via dividing the total hours of sleep by total hours in the bed multiplied by 100. Then, the fifth component deals with sleep disorders and is achieved by calculating the mean value of Question 5. The sixth component is related to hypnotic drugs and is determined based on Question 6. Finally, the seventh component captures inadequate performance throughout the day and is determined by two questions (mean scores of Questions 7 and 8). Each question is rated between 0 and 3 points where maximum score for each component is 3. The total scores range of the seven components making up the total score range from 0 to 21. Higher scores represent a lower sleep quality, where a score above 6 indicates poor sleep quality. The reliability and validity of this inventory have also been approved in Iran, where the Cronbach's alpha coefficient of the questionnaire was 0.78 to 0.82.18 In another study, Cronbach's alpha for the Persian version was 0.77. In cut-off point 5, the sensitivity and specificity were 94% and 72%, and in cut-off point 6, they were 85% and 84%, respectively.19

After collecting the questionnaires and introducing students' demographic data to a computer using SPSS version 16, the relationship between sleep quality scores and grade point average (GPA high and low) was calculated.

Results

The results indicated that 34 cases (33.3%) of the subjects were male. The mean age of the sample 23.10 ± 3.25, where the mean age for females was 22.46± 2.44 and for males was 24.38± 4.19. The participants in the study came from various disciplines including laboratory science, medicine, pharmacology, emergency medicine, obstetrics, radiology, operating room, health technology, and nursing.

Most students lived in dormitories (50%) and 46.1% at home, with 3.9% living in rental houses. The students' educational level ranged between the third semester and twelfth semester.

Among those participating in the study, 67 patients (65.7%) consumed coffee, 90 cases (88.2%) used tea, and 1 (1%) took a drug.

For comparing the mean scores of students and the component of sleep, Spearman test (non-normal data) was employed, where a significant correlation was observed between GPA and hours taking to fall asleep ( Table 1 ).

Table 1

Sleep Components

p value

Correlation Coefficient

Similarly, there was a relationship between sleep components and tea, coffee, hypnotic drugs, and drug ( Table 2 ).

Table 2

Sleep Components

Type of Use

p value

Correlation Coefficient

Coffee

0.88

Drugs

0.64

Coffee

0.99

Drugs

1

Coffee

0.001

Drugs

0.69

Coffee

0.14

Drugs

0.33

Coffee

0.31

Drugs

0.72

Coffee

0.001

Drugs

0.16

Coffee

0.9

Drugs

0.66

Coffee

0.96

Drugs

0.71

Coffee

0.75

Drugs

0.14

Coffee

0.88

Drugs

0.64

On the other hand, independent t-test between Pittsburgh scores in the two groups did not show any significant differences. Nevertheless, impaired sleep quality was moderate to severe in both groups ( Table 3 ).

Table 3

No

Total Pittsburg Scores

Total

Mean

Standard Deviation

p value

Normal

Weak

Moderate

Severe

Group

Low

4

9

10

21

44

13.5

6.808

0.875

High

0

9

22

27

58

13.28

4.319

Total

4

18

32

48

102

Discussion

The results indicated that impaired sleep quality between the two groups was not statistically significant. Although the relationship between sleeping and academic success has been introduced in medical literature since a long time, there still no definitive answer in this case. In a meta-analysis study conducted to examine the impact of sleep quality, sleep duration, and sleepiness on adolescents' academic performance, although all three variables were related to academic achievement (positive relationship between sleep quality and duration of sleep and negative association with sleepiness), this relationship was very trivial.1

On the other hand, another systematic review study of descriptive studies concluded that sleep disturbance adversely affects different areas such as general health, social status, and academic performance. However, longitudinal studies are required for a more accurate examination.20,21 In an another systematic review of other authors, the authors concluded that under-sleeping would have an impact on learning of some students, and could have a detrimental effect on academic achievement.22 Further, another review study also suggests a conclusive recommendation which has to be done to modify sleep so that it can be used for academic success.23

The present study was conducted to explore whether sleep disorder can influence academic achievement or not. Accordingly, a specific sample of accomplished or unachieved students were selected to compare the quality and quantity of sleep. However, no significant difference was between the two groups. Other studies have reached similar conclusions.

Sweileh and his colleagues in a study on 400 Palestinian students concluded that academic achievement was not correlated with sleep quality.15 In another study on 189 medical students in Pakistan, there was no significant association between lack of sleep and test scores.16 In this regard, there is a possibility of sleep disorder in students, and this possibility has been expressed for the lack of academic achievement, but it has not been clearly explained.11 In another study, sleepiness during the day (not the quality and quantity of sleep) was identified as an independent predictor of academic success.5 In a similar study again the time it takes to fall sleep and the wake-up time (not the total amount of sleep) were associated with academic success,14 where the total amount of sleep in adolescents with a dynamic mind was not related to their academic achievement.24 In contrast to such studies that emphasize lack of association or low association, there are other studies that have observed an inverse relationship between sleep disturbance and academic achievement. In a study on 491 first-, second-, and third-year medical students, there was a correlation between academic performance and the amount of nighttime sleep as well as daytime sleepiness.25 In a similar study on medical students, lack of sleep at night, late going to bed, and daytime sleepiness had a negative effect on the academic performance of the students.26 Notably, sleep disturbances are likely to yield a negative impact on academic performance, thereby causing a vicious cycle.25,27 Taken together, the studies suggest that most studies have mentioned poor quality sleep among the majority of students.3,26,27 Accordingly, concluding the relationship between common sleep disturbance and academic performance should be done with caution. The reason is that academic success can be affected by different factors including the level of family income, the evolutionary process, intake of supplements and vitamins, family size, social media dependency, addiction to social networks, and social issues. In studies these extraneous factors are not under control, thus emphasizing the fact that the presence or absence of correlation between sleep quality and academic performance should be done with caution and using longitudinal studies.

Limitations

The main limitation of this study was the small sample size, but a specific sampling method was chosen to overcome this shortcoming. Another limitation of the study was not controlling for confounding factors in the study. Based on the results of this study and similar studies, further research should be conducted with a better design.

Conclusion

The results indicated no significant difference between sleep quality in achieved and unachieved academic performance. Nevertheless, to conclude with more certainty, longitudinal studies should be performed to control confounding factors.

Acknowledgments

The authors of this article appreciate the collaborations of the Sleep Disorders Research Center.

Funding Statement

Funding for this research was provided by the Kermanshah University of Medical Sciences, Sleep Disorders Research Center (93026).

Data Sharing Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

Informed consent obtained from all participants in the study and this study conducted by the Sleep Disorders Research Center. Identity letter obtained from deputy of research and technology to collecting data. Ethics approval was received from the ethics committee of deputy of research and technology - Kermanshah University of Medical Sciences, number 93026 on 6 April 2013.

Disclosure

The authors declare that they have no conflict of interest.

References

4. Scott E, Carrell, Teny Maghakian EJ. A's from Zzzz's? The Causal effect of school start time on the academic achievement of adolescents. [ West American Economic J. 2011;3(3):62-81. Google Scholar]

16. Kazim M. Sleep patterns and academic performance in students of a medical college in Pakistan. [ KUST Med J. 2011;3(2):57-60. Google Scholar]

18. Hasanzadeh M., Alavi K.N.K, Ghalehbandi M.F. Sleep quality in Iranian drivers recognized as responsible for severe road accidents. [ J Res Behav Sci. 2008;6(2):97-107. Google Scholar]

24. Johnston A, Gradisar M, Dohnt H, Billows M, Mccappin S. Adolescent sleep and fluid intelligence performance. [ Sleep Biol Rhythms. 2010;8(3):180-186. doi: 10.1111/j.1479-8425.2010.00442.x [ CrossRef] Google Scholar]

Articles from Advances in Medical Education and Practice are provided here courtesy of Dove Press

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The Effect of Sleep Quality on Students' Academic Achievement

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Sleep is an inseparable part of human health and life, which is crucial in learning, practice, as well as physical and mental health. It affects the capacity of individual learning, academic performance, and neural-behavioral functions. This study aimed ...

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Transforming Education: The Impact of Digital Technologies and School Capacity

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Transforming Education: The Impact of Digital Technologies and School Capacity

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In what ways do digital technologies redefine educational landscapes, and how do varying factors shape schools' capacity for effective digital transformation?

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Abstract

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools' digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students' performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Keywords : Digital technologies, Education, Impact, Digital capacity, Digital transformation, K-12

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021; OECD, 2021). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020; Lawrence & Tar, 2018) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015; Lawrence & Tar, 2018). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021; König et al., 2020). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021; Di Pietro et al, 2020). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020) and increase their digitalization levels (Costa et al., 2021). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021; Rott & Marouane, 2018) and touches many aspects of a school's development (Delcker & Ifenthaler, 2021) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021). Namely, digitalization refers to " a series of deep and coordinated culture, workforce, and technology shifts and operating models" (Brooks & McCormack, 2020, p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary " culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices" (Costa et al, 2021, p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school's digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school's digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools' digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: "impact" OR "effects" AND "digital technologies" AND "education", "impact" OR "effects" AND "ICT" AND "education". We further refined our results by adding the terms "meta-analysis" and "review" or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012; Tolani-Brown et al., 2011).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​Table1 1 .

Table 1

Inclusion criteria

Exclusion criteria

* Published in 2005 or later

* Ph.D. dissertations and theses

* Conference poster papers

* Conference papers without proceedings

* Resources on higher education

* Resources on pre-school education

* Individual studies

* Review and meta-analysis studies

* Formal education K-12

* Peer-reviewed articles

* Articles in English

* Reports from professional/international bodies

* Governmental reports

* Book chapters

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as " a significant influence or effect of ICT on the measured or perceived quality of (parts of) education" (Balanskat, 2009, p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

Findings

The study's results identified multiple dimensions of the impact of digital technologies on students' knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers' professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools' digital capacity and transformation, such as digital competencies, the teachers' personal characteristics and professional development, as well as the school's leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students' knowledge, skills, attitudes, and emotions

The impact of ICT use on students' knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes-used to supplement rather than substitute instruction - could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5-18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014) reported medium-low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students' notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016) reported a small positive effect of one-to-one laptop programs on students' academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022) who investigated the effect of touchscreen technologies on young students' learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children's learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students' performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022; Wang et al., 2022). However, although Talan et al. ( 2020) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students' learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b). Villena-Taranilla et al. ( 2022) noted the moderate effect of VR technologies on students' learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022) highlighted the role of immersive VR, since its effect on students' learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022). The impact of AI on education was investigated by Su and Yang ( 2022) and Su et al. ( 2022), who showed that this technology significantly improved students' understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students' literacy skills and reported positive effects on language learning (Balanskat et al., 2006; Grgurović et al., 2013; Friedel et al., 2013; Zheng et al., 2016; Chen et al., 2022b; Savva et al., 2022). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014), writing (Higgins et al., 2012; Wen & Walters, 2022; Zheng et al., 2016), as well as reading and comprehension (Cheung & Slavin, 2011; Liao et al., 2007; Schwabe et al., 2022). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022; Bado, 2022; Villena-Taranilla et al., 2022; Wang et al., 2022). Specifically, a number of studies reported positive impacts on students' achievement in mathematics (Balanskat et al., 2006; Hillmayr et al., 2020; Li & Ma, 2010; Pan et al., 2022; Ran et al., 2022; Verschaffel et al., 2019; Zheng et al., 2016). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006; Liao et al., 2007; Zheng et al., 2016; Hillmayr et al., 2020; Kalemkuş & Kalemkuş, 2022; Lei et al., 2022a). Çelik ( 2022) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students' achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017; Condie & Munro, 2007), and design and technology (Balanskat et al., 2006).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012), metacognitive learning outcomes (Verschaffel et al., 2019), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022; Su & Yang, 2022; Su et al., 2022). Additionally, several investigations have reported benefits from the use of ICT on students' creativity (Fielding & Murcia, 2022; Liu et al., 2022; Quah & Ng, 2022). Lastly, digital technologies were also found to be beneficial for enhancing students' lifelong learning skills (Haleem et al., 2022).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019; Fadda et al., 2022) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a). Chen et al. ( 2022a) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022) also documented that AI technologies effectively strengthened students' attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students' knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh's meta-analysis ( 2022) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students' social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls' achievement was smaller than that on boys'. Along the same lines, Arztmann et al. ( 2022) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers' professional and teaching practices

Various research studies have explored the impact of ICT on teachers' instructional practices and student assessment. Friedel et al. ( 2013) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022). Specifically, Bado ( 2022) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students' learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students' learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students' data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers' digital competence. Balanskat et al. ( 2006) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers' basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers' confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students' work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers' isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006).

Zheng et al. ( 2016) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students' grades, which generated positive results on students' learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students' performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

Table 2

Impacts

References

Students

Knowledge, skills, attitudes, and emotions

* Learning gains from the use of ICTs across the curriculum

Eng, 2005; Balanskat et al., 2006; Liao et al., 2007; Tamim et al., 2011; Higgins et al., 2012; Chauhan, 2017; Sung et al., 2016; Schmid et al., 2014; Tamim et al., 2015; Zheng et al., 2016; Haßler et al., 2016; Kalati & Kim, 2022; Martinez et al., 2022; Talan et al., 2020; Panet al., 2022; Garzón & Acevedo, 2019; Garzón et al., 2020; Villena-Taranilla, et al., 2022; Coban et al., 2022

* Positive learning gains from the use of ICTs in specific school subjects (e.g., mathematics, literacy, language, science)

Arztmann et al., 2022; Villena-Taranilla, et al., 2022; Chen et al., 2022b; Balanskat et al., 2006; Grgurović, et al., 2013; Friedel et al., 2013; Zheng et al., 2016; Savva et al., 2022; Kao, 2014; Higgins et al., 2012; Wen & Walters, 2022; Liao et al., 2007; Cheung & Slavin, 2011; Schwabe et al., 2022; Li & Ma, 2010; Verschaffel et al., 2019; Ran et al., 2022; Liao et al., 2007; Hillmayr et al., 2020; Kalemkuş & Kalemkuş, 2022; Lei et al., 2022b; Condie & Munro, 2007; Chauhan, 2017; Bado, 2022; Wang et al., 2022; Pan et al., 2022

* Positive learning gains for special needs students and low-achieving students

Eng, 2005; Balanskat et al., 2006; Punie et al., 2006; Koh, 2022

* Oportunities to develop a range of skills (e.g., subject-related skills, communication skills, negotiation skills, emotion control skills, organizational skills, critical thinking skills, creativity, metacognitive skills, life, and career skills)

Balanskat et al., 2006; Fu, 2013; Tamim et al., 2015; Zheng et al., 2016; Higgins et al., 2012; Verschaffel et al., 2019; Su & Yang, 2022; Su et al., 2022; Lu et al., 2022; Liu et al., 2022; Quah & Ng, 2022; Fielding & Murcia, 2022; Tang et al., 2022; Haleem et al., 2022

* Oportunities to develop digital skills (e.g., information skills, media skills, ICT skills)

Zheng et al., 2016; Su & Yang, 2022; Lu et al., 2022; Su et al., 2022

* Positive attitudes and behaviours towards ICTs, positive emotions (e.g., increased interest, motivation, attention, engagement, confidence, reduced anxiety, positive achievement emotions, reduction in bullying and cyberbullying)

Balanskat et al., 2006; Schmid et al., 2014; Zheng et al., 2016; Fadda et al., 2022; Higgins et al., 2012; Chen et al., 2022a; Lei et al., 2022b; Arztmann et al., 2022; Su et al., 2022

Learning experience

* Enhance access to resources

Jewitt et al., 2011; Fu, 2013

* Opportunities to experience various learning practices (e.g., active learning, learner-centred learning, independent and personalized learning, collaborative learning, self-directed learning, self- and peer-review)

Jewitt et al., 2011; Fu, 2013

Equality, inclusion, and social integration

* Improved communication, functional skills, participation, self-esteem, and engagement of special needs students

Condie & Munro, 2007; Baragash et al., 2022; Koh, 2022

* Benefits for both girls and boys

Zheng et al., 2016; Arztmann et al., 2022

Teachers

Professional practice

Teaching practice

* Application of different instructional practices (e.g., scaffolding, synchronous collaborative learning, online learning, blended learning, hybrid learning)

Friedel et al., 2013; Bado, 2022; Kazu & Yalçin, 2022; Ulum, 2022

Administrators

Data-based decision-making

Organizational processes

Home-school communication

School leaders

Professional practice

Parents

Home-school relationships

Table 3

Technologies/tools/practices/policies

References

ICT general - various types of technologies

Practices/policies on schools' digital transformation

Computer-assisted instruction, computer simulations, activeboards, and web-based learning

Liao et al., 2007 (meta-analysis)

Tamim et al., 2011 (meta-analysis)

Çelik, 2022 (review)

Moran et al., 2008 (meta-analysis)

Eng, 2005 (review)

Mobile devices-touch screens (smart devices, tablets, laptops)

Digital games (various types e.g., adventure, serious; various domains e.g., history, science)

Augmented reality (AR)

Garzón & Acevedo, 2019 (meta-analysis)

Garzón et al., 2020 (meta-analysis and research synthesis)

Kalemkuş & Kalemkuş, 2022 (meta-analysis)

Baragash et al., 2022 (meta-analysis)

Virtual reality (VR)

Villena-Taranilla et al., 2022 (meta-analysis)

Chen et al., 2022b (meta-analysis)

Coban et al., 2022 (meta-analysis)

Immersive virtual reality (IVR)

Artificial intelligence (AI) and robotics

Su & Yang, 2022 (review)

Su et al., 2022 (meta review)

Online learning/elearning

Ulum, 2022 (meta-analysis)

Cheok & Wong, 2015 (review)

Electronic books/digital storytelling

Savva et al., 2022 (meta-analysis)

Quah & Ng, 2022 (systematic review)

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students' learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners' characteristics (Underwood, 2009; Tamim et al., 2011; Higgins et al., 2012; Archer et al., 2014; Sung et al., 2016; Haßler et al., 2016; Chauhan, 2017; Lee et al., 2020; Tang et al., 2022).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students' skills on how to use them. Fu ( 2013) found that students' lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers' professional development. Higgins et al. ( 2012) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015), meanwhile, reported studies that showed a strong positive association between teachers' computer skills and students' use of computers. Teachers' lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006; Delgado et al., 2015).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006), or they prefer applying "safe" technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007). Apart from digital competence, technical support in the school setting has also been shown to affect teachers' use of technology in their classrooms (Delgado et al., 2015). Ferrari et al. ( 2011) found that while teachers' use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017).

Teachers' personal characteristics, training approaches, and professional development

Teachers' personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015) found that teachers' personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers' levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020) documented that training teachers on ICT had an important effecton students' learning.

According to Balanskat et al. ( 2006), the impact of ICTs on students' learning is highly dependent on the teachers' capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010) and Hardman ( 2019) showed that the positive effect of technology on students' achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019) and Garzón et al. ( 2020) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students' learning gains. Garzón and Acevedo ( 2019) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students' learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013) found that providing training and support to teachers increased the positive impact of the interventions on students' learning gains. Trucano ( 2005) argued that positive impacts occur when digital technologies are used to enhance teachers' existing pedagogical philosophies. Higgins et al. ( 2012) found that the types of technologies used and how they are used could also affect students' learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012). Archer et al. ( 2014) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students' learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013) reported that the lack of teachers' knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers' motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018). Zheng et al. ( 2016) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school's technological maturity (European Commission, 2019). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007) emphasized the importance of the principal's enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019, p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015). For example, Zheng et al. ( 2016) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009). Bingimlas ( 2009) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006; Bingimlas, 2009).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006) reported that government interventions and training programs increased teachers' enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration's workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015) reported that data infrastructure in education is developing due to the demand for " information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices" (p. 771). In this regard, practices, such as datafication which refers to the " translation of information about all kinds of things and processes into quantified formats" have become essential for decision-making based on accountability reports about the school's quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students' online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020).

Students' socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school's work can make a difference to their children's attitudes towards learning and, as a result, their achievement (Hattie, 2008). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017). The European Commission ( 2020) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006). Trucano documented that the use of computers at home positively influenced students' confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006; Underwood, 2009). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020).

Schools' socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school's digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure 1 summarizes the factors affecting the digital technologies' impact on school stakeholders based on the findings from the literature review.

Discussion

The findings revealed that the use of digital technologies in education affects a variety of actors within a school's ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure 2 summarizes the trends identified in current research around the impact of digital technologies on schools' digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005; Liao et al., 2007; Moran et al., 2008; Tamim et al., 2011), moving towards the use of learning platforms (Jewitt et al., 2011), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015; Sung et al., 2016; Talan et al., 2020), as well as e-books (e.g., Savva et al., 2022), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019; Garzón et al., 2020; Kalemkuş & Kalemkuş, 2022), or robotics and AI (e.g., Su & Yang, 2022; Su et al., 2022). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015; Kazu & Yalçin, 2022; Ulum, 2022). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students' learning outcomes has effects that are small (Coban et al., 2022; Eng, 2005; Higgins et al., 2012; Schmid et al., 2014; Tamim et al., 2015; Zheng et al., 2016) to moderate (Garzón & Acevedo, 2019; Garzón et al., 2020; Liao et al., 2007; Sung et al., 2016; Talan et al., 2020; Wen & Walters, 2022). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022).

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011; Higgins et al., 2012; Archer et al., 2014; Sung et al., 2016; Haßler et al., 2016; Chauhan, 2017; Lee et al., 2020; Lei et al., 2022a). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017; Condie & Munro, 2007). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers' instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022; Schmid et al., 2014; Tamim et al., 2015). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers' personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students' socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013; Higgins et al., 2012). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school's ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school's digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools' digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as "policies and strategies for ICT integration in education". Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE-developed on the DigCompOrg framework- (Kampylis et al., 2015; Bocconi & Lightfoot, 2021) can help capture a school's digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019; Chapman & Sammons, 2013; De Silva et al., 2014).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students' learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students' and teachers' digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools' digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Footnotes

Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Articles from Education and Information Technologies are provided here courtesy of Nature Publishing Group

# data[].attributes.attachment_link.title
Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

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Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ...

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The Crucial Role of Mental Health in Student Success

# data[].attributes.subject
The Crucial Role of Mental Health in Student Success

# data[].attributes.body
How does prioritizing mental health among students enhance academic performance and overall well-being in educational settings?

# data[].attributes.attachment_link.readable_text
Human beings have a mind-body connection. This means our mental, physical, and social health affect each other. For students, mental health is important because it impacts how they learn and participate in school.

Mental health affects students':

Last year, the American Academy of Pediatrics and two other child health institutions joined in declaring that child and adolescent mental health is a national emergency. This means understanding student mental health is more critical than ever.

This blog post will explore the important connection between school and student mental health. We will focus on the following factors:

What is Mental Health?

Mental health includes our emotional, social, and psychological well-being. It affects how we think and act and how we interact with others.

The benefits of good mental health in students include:

Why is Mental Health Important for Students?

1. Student Academic Achievement

A student's mental health affects their academic performance. Students with good mental health are prepared to learn. They tend to have higher self-esteem and be motivated to achieve educational goals.

A student struggling with their mental health or diagnosed with a mental health disorder may have trouble paying attention, remembering, and problem-solving. They may also have trouble meeting classroom expectations.

When students are disruptive, uninterested, or defiant, school staff disciplines them. Exclusionary punishments like suspensions and expulsions have a negative impact on academic achievement. Students can't learn and participate if they are excluded from school.

Mental health also affects a student's attendance and even graduation rates. Connecting students to mental health services helps them stay in school.

According to the American Psychiatric Association, "fifty percent of mental illness begins by age 14, and three-quarters begins by age 24."

For most people who develop a mental health disorder, symptoms start while they are still in school. The sooner a mental health concern is found and treated, the better. Left untreated, mental health disorders will get worse. They can last throughout a student's school years and into adulthood.

Parents, teachers, and school administration can change a student's life trajectory by recognizing when someone needs help and connecting them with support.

3. Meeting Student Mental Health Needs

Some student populations have greater mental health needs than others.

These students may:

Schools can deliver mental health services or resources to students who need them most. This helps students stay in school and stay engaged in learning.

Student Populations with the Greatest Mental Health Needs

Non-white students, students with public insurance, and students from low-income households are less likely to access private or independent mental health services. These students must rely on their schools for mental health services. Compare this list to the list above. You can see that there is a crossover between students who need mental health services the most and students who have the fewest options for services.

It is important to remember that a student's environment affects their mental health. Supportive school environments help students thrive. When students need help with their mental health but don't receive support, their mental health gets worse.

4. Mental Health Awareness and Education

One way that schools can help students is with mental health education. Quality mental health education for students includes:

How to support student mental health

How to recognize mental health symptoms and warning signs

How and where to get help

Life skills education programs are also a chance for educators to combat mental health myths and reduce mental health stigma.

How to Support Mental Health

The following are ways that students can care for their mental health:

Common Mental Health Myths

Mental health education can overcome common myths such as:

Children don't have mental health disorders.

Mental illness is a choice.

Medication doesn't help mental disorders.

All these myths are false. Misinformation about mental health is dangerous for students.

Getting Help with Mental Health
Your Own Mental Health
Students struggling with their mental health should talk to someone they trust. This could be a parent, teacher, or counselor. If the student feels unsafe or overwhelmed, they can contact a crisis hotline.

Guidance counselors at school are trained professionals to help with mental health. Here are ways you could start the conversation:
Supporting Someone Else
If you see someone else struggling with their mental health, the first thing to remember is you are not a counselor. You can offer support, but you also want to connect them with a professional.

You can listen to them, share resources, and remind them that you care. Also, remember that not everyone wants help. Respect a person's wishes if they do not want to talk about their situation.

Mental Health Resources

Untreated mental health disorders can have dangerous outcomes. These include self-harm, bullying, substance use, and suicide. Schools play a critical role in preventing these outcomes.

Through early detection, meeting mental health needs, education, and awareness, schools can help students have healthy, happy, and productive lives.

What is a Mental Health Disorder?

A mental health disorder, also known as a mental disorder or mental illness, is a diagnosable health condition. A mental health disorder changes how a person thinks, feels, or behaves, as well as causing distress and disruption in a person's life.

Mental health disorders can range from mild to severe. While there is no specific cause, mental health is affected by:

our biology (genes and brain chemistry),

our environment and experiences (home life, trauma, abuse), and

our lifestyle choices (nutrition, sleep, substance use).

Common Mental Health Disorders in Students

According to the National Alliance on Mental Illness, approximately one in six children in the United States experience a mental health disorder. In college-aged students (young adults 18-25 years old), that number jumps up to one in three.

According to the CDC, the most diagnosed mental health disorders in students are:

Warning Signs of Mental Health Disorders in Students

If a student is struggling with their mental health, there will be warning signs. Sometimes these are signs that the student needs help caring for their mental health. The warning signs could also be symptoms of a diagnosable mental health disorder.

The following are signs and symptoms of a student struggling with their mental health:

When these signs and symptoms disrupt how we function at home, school, work, or in our relationships, it is time to get professional help.

If left untreated, mental health disorders can lead to more serious problems, including:

Fortunately, schools can offer resources, referrals, and services to support student mental health.

Next Steps: Mental Health Education for Students

Mental health is important for students because it is part of overall well-being. Good mental health promotes a student's health, happiness, learning, and academic achievement. Through mental health education, schools can combat mental health stigma and common myths. Students can learn about improving their mental health, signs of mental health disorders, and where to go when they need help.

McMillen Health's educators would love to work with you and your students to promote mental health awareness. Using high-tech media rooms, our educators can reach classrooms anywhere with an internet connection.

Mental Health Programs Offered at McMillen Health

Alysia Marshall-Seslar

Alysia Marshall-Seslar is the Writing and Marketing Associate at McMillen Health. Along with being the author of TamTalks, Alysia contributes to the research and development of McMillen's custom health education curriculum.

Human beings have a mind-body connection. This means our mental, physical, and social health affect each other. For students, mental health is important because it impacts how they learn and participate in school.

Mental health affects students':

Last year, the American Academy of Pediatrics and two other child health institutions joined in declaring that child and adolescent mental health is a national emergency. This means understanding student mental health is more critical than ever.

This blog post will explore the important connection between school and student mental health. We will focus on the following factors:

What is Mental Health?

Mental health includes our emotional, social, and psychological well-being. It affects how we think and act and how we interact with others.

The benefits of good mental health in students include:

Why is Mental Health Important for Students?

1. Student Academic Achievement

A student's mental health affects their academic performance. Students with good mental health are prepared to learn. They tend to have higher self-esteem and be motivated to achieve educational goals.

A student struggling with their mental health or diagnosed with a mental health disorder may have trouble paying attention, remembering, and problem-solving. They may also have trouble meeting classroom expectations.

When students are disruptive, uninterested, or defiant, school staff disciplines them. Exclusionary punishments like suspensions and expulsions have a negative impact on academic achievement. Students can't learn and participate if they are excluded from school.

Mental health also affects a student's attendance and even graduation rates. Connecting students to mental health services helps them stay in school.

According to the American Psychiatric Association, "fifty percent of mental illness begins by age 14, and three-quarters begins by age 24."

For most people who develop a mental health disorder, symptoms start while they are still in school. The sooner a mental health concern is found and treated, the better. Left untreated, mental health disorders will get worse. They can last throughout a student's school years and into adulthood.

Parents, teachers, and school administration can change a student's life trajectory by recognizing when someone needs help and connecting them with support.

3. Meeting Student Mental Health Needs

Some student populations have greater mental health needs than others.

These students may:

Schools can deliver mental health services or resources to students who need them most. This helps students stay in school and stay engaged in learning.

Student Populations with the Greatest Mental Health Needs

Non-white students, students with public insurance, and students from low-income households are less likely to access private or independent mental health services. These students must rely on their schools for mental health services. Compare this list to the list above. You can see that there is a crossover between students who need mental health services the most and students who have the fewest options for services.

It is important to remember that a student's environment affects their mental health. Supportive school environments help students thrive. When students need help with their mental health but don't receive support, their mental health gets worse.

4. Mental Health Awareness and Education

One way that schools can help students is with mental health education. Quality mental health education for students includes:

How to support student mental health

How to recognize mental health symptoms and warning signs

How and where to get help

Life skills education programs are also a chance for educators to combat mental health myths and reduce mental health stigma.

How to Support Mental Health

The following are ways that students can care for their mental health:

Common Mental Health Myths

Mental health education can overcome common myths such as:

Children don't have mental health disorders.

Mental illness is a choice.

Medication doesn't help mental disorders.

All these myths are false. Misinformation about mental health is dangerous for students.

Getting Help with Mental Health
Your Own Mental Health
Students struggling with their mental health should talk to someone they trust. This could be a parent, teacher, or counselor. If the student feels unsafe or overwhelmed, they can contact a crisis hotline.

Guidance counselors at school are trained professionals to help with mental health. Here are ways you could start the conversation:
Supporting Someone Else
If you see someone else struggling with their mental health, the first thing to remember is you are not a counselor. You can offer support, but you also want to connect them with a professional.

You can listen to them, share resources, and remind them that you care. Also, remember that not everyone wants help. Respect a person's wishes if they do not want to talk about their situation.

Mental Health Resources

Untreated mental health disorders can have dangerous outcomes. These include self-harm, bullying, substance use, and suicide. Schools play a critical role in preventing these outcomes.

Through early detection, meeting mental health needs, education, and awareness, schools can help students have healthy, happy, and productive lives.

What is a Mental Health Disorder?

A mental health disorder, also known as a mental disorder or mental illness, is a diagnosable health condition. A mental health disorder changes how a person thinks, feels, or behaves, as well as causing distress and disruption in a person's life.

Mental health disorders can range from mild to severe. While there is no specific cause, mental health is affected by:

our biology (genes and brain chemistry),

our environment and experiences (home life, trauma, abuse), and

our lifestyle choices (nutrition, sleep, substance use).

Common Mental Health Disorders in Students

According to the National Alliance on Mental Illness, approximately one in six children in the United States experience a mental health disorder. In college-aged students (young adults 18-25 years old), that number jumps up to one in three.

According to the CDC, the most diagnosed mental health disorders in students are:

Warning Signs of Mental Health Disorders in Students

If a student is struggling with their mental health, there will be warning signs. Sometimes these are signs that the student needs help caring for their mental health. The warning signs could also be symptoms of a diagnosable mental health disorder.

The following are signs and symptoms of a student struggling with their mental health:

When these signs and symptoms disrupt how we function at home, school, work, or in our relationships, it is time to get professional help.

If left untreated, mental health disorders can lead to more serious problems, including:

Fortunately, schools can offer resources, referrals, and services to support student mental health.

Next Steps: Mental Health Education for Students

Mental health is important for students because it is part of overall well-being. Good mental health promotes a student's health, happiness, learning, and academic achievement. Through mental health education, schools can combat mental health stigma and common myths. Students can learn about improving their mental health, signs of mental health disorders, and where to go when they need help.

McMillen Health's educators would love to work with you and your students to promote mental health awareness. Using high-tech media rooms, our educators can reach classrooms anywhere with an internet connection.

Mental Health Programs Offered at McMillen Health

Alysia Marshall-Seslar

Alysia Marshall-Seslar is the Writing and Marketing Associate at McMillen Health. Along with being the author of TamTalks, Alysia contributes to the research and development of McMillen's custom health education curriculum.

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Why Mental Health is Important for Students - McMillen Health

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Student mental health has a direct impact on learning, school participation, and academic achievement. Teachers, schools, and educational organizations can support student success by promoting mental health.

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How Climate Change matters on Agricultural Production in Asia?

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How Climate Change matters on Agricultural Production in Asia?

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To what extent does climate change threaten agricultural production in Asia, and can innovative practices mitigate its impact?

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Muhammad Habib-ur-Rahman, 1 , 2 , * Ashfaq Ahmad, 3 , 4 Ahsan Raza, 1 Muhammad Usama Hasnain, 4 Hesham F. Alharby, 5 , * Yahya M. Alzahrani, 5 Atif A. Bamagoos, 5 Khalid Rehman Hakeem, 5 , 6 , 7 Saeed Ahmad, 8 , 9 Wajid Nasim, 9 Shafaqat Ali, 10 Fatma Mansour, 11 and Ayman EL Sabagh 12 , 13 , *

Abstract

Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040-2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040-2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

Keywords: climate variability, yield reduction, livestock, elevated temperature, adaptation, climate and crop modeling, decision support system, sustainable production

Introduction

Asia is the most populous subcontinent in the world (UNO, 2015), comprising 4.5 billion people-about 60% of the total world population. Almost 70% of the total population lives in rural areas and 75% of the rural population are poor and most at risk due to climate change, particularly in arid and semi-arid regions (Yadav and Lal, 2018; Population of Asia, 2019). The population in Asia is projected to reach up to 5.2 billion by 2050, and it is, therefore, challenging to meet the food demands and ensure food security in Asia (Rao et al., 2019). In this context, Asia is the region most likely to attribute to population growth rate, and more prone to higher temperatures, drought, flooding, and rising sea level (Guo et al., 2018; Hasnat et al., 2019). In Asia, diversification in income of small and poor farmers and increasing urbanization is shocking for agricultural productivity. Asia is the home of a third of the world's population and the majority of poor families, most of which are engaged in agriculture (World Bank, 2018). We can expect diversification of adverse climate change effects on the agriculture sector due to diversity of farming and cropping systems with dependence on climate. According to the sixth assessment report of IPCC, higher risks of flood and drought make Asian agricultural productivity highly susceptible to changing climate (IPCC, 2019). Climate change has already adversely affected economic growth and development in Asia, although there is low emission of greenhouse gasses (GHG) in this region (Gouldson et al., 2016; Ahmed et al., 2019a). Still, China and India are major contributors to global carbon dioxide emission; the share of each Asian country in cumulative global carbon dioxide emission is presented in Figures 1 , ​,2. 2 . Although GHGs emission from the agriculture sector is lower than the others, it still has a negative impact. Emission of GHGs from different agricultural components and contribution to emissions can be found in Figure 3 . However, the contribution of Asian countries in GHGs including land use changes and forestry is described in Figure 4 .

Asia is facing alarming challenges due to climate change and variability as illustrated by various climatic models predicting the global mean temperature will increase by 1.5°C between 2030 and 2050 if it continues to increase at the current rate (IPCC, 2019). In arid areas of the western part of China, Pakistan, and India, it is also projected that there will be a significant increase in temperature (IPCC, 2019). During monsoon season, there would be an increase in erratic rainfall of high intensity across the region. In South and Southeast Asia, there would be an increase in aridity due to a reduction in winter rainfall. Due to climatic abnormalities, there will be a 0.1 m increase in sea level by 2,100 across the globe (IPCC, 2019). In Asia, an increase in heat waves, hot and dry days, and erratic and unsure rainfall patterns is projected, while dust storms and tropical cyclones are predicted to be worse in the future (Gouldson et al., 2016). Natural disasters are the main reason behind the agricultural productivity (crops and livestock) losses in Asia, including extreme temperature, storms and wildfires (23%), floods (37%), drought (19%), and pest and animal diseases infestation (9%) which accounted for 10 USD billions in amount (FAO, 2015). During the last few decades, tropical cyclones in the Pacific have occurred with increased frequency and intensity. South Asia consisted of 262 million malnourished inhabitants, which made South Asia the most food insecure region across the globe (FAO, 2015; Rasul et al., 2019). In remote dry lands and deserts, the rural population is more vulnerable to climate change due to the scarcity of natural resources.

In Asia, climate variability (temperature and rainfall) and climate-driven extremes (flood, drought, heat stress, cold waves, and storms) have several negative impacts on the agriculture sector (FAO, 2016), especially in the cropping system which has a major role in food security, and thus created the food security issues and challenges in Asia (Cai et al., 2016; Aryal et al., 2019). The rice-wheat cropping system, a major cropping system which fills half of the food demand in Asia, is under threat due to climate change (Ghaffar et al., 2022). Climate change adversely affects both the quantity and quality of wheat and rice crops (Din et al., 2022; Wasaya et al., 2022). For instance, the protein content and grain yield of wheat have been reduced because of the negative impacts of increasing temperature (Asseng et al., 2019). The temperature rise has decreased the crop-growing period, and crop evapotranspiration ultimately reduced wheat yield (Azad et al., 2018). Adverse impacts of climate change and variability on winter wheat yield in China are attributed to increased average temperature during the growing period (Geng et al., 2019). Climate change is also adversely affecting the quality traits especially protein content, and sugars and starch percentages in grains of wheat. Elevated carbon dioxide and high temperatures increase the growth traits while decreasing the protein content in wheat grains (Asseng et al., 2019). Similarly, drought stress also reduces the protein content and soluble sugars of the wheat crop (Rakszegi et al., 2019; Hussein et al., 2022). The decline in the starch content in wheat grains has also been observed under drought stress (Noori and Taliman, 2022). Similarly, heat stress also causes a decline in the protein content, soluble sugar, and starch content in wheat grains (Zahra et al., 2021; Iqbal et al., 2022; Zhao et al., 2022). Climate change also negatively affects the quality of wheat products as the rise in temperature causes a reduction in protein content, sugars, and starch. It is assessed that rise in temperature by 1-4°C could decrease the wheat yield up to 17.6% in the Egyptian North Nile Delta (Kheir et al., 2019). In China, crop phenology has changed because of both climate variability and crop management practices (Liu et al., 2018). Both climate change scenarios and human management practices have adversely affected wheat phenology in India and China (Lv et al., 2013; Ren et al., 2019). The elevated temperature has increased the infestation of the aphid population on wheat crops and ultimately reduced yield (Tian et al., 2019). There is a direct and strong correlation between diseases attached to climate change. For instance, the Fusarium head blight of wheat crops is caused by the Fusarium species and its chances of an attack were increased due to high humidity and hot environment (Shah et al., 2018). A similar study has shown a direct interaction between insect pests and diseases and higher temperature and carbon dioxide levels in rice production (Iannella et al., 2021; Tan et al., 2021; Tonnang et al., 2022).

Climate variability has marked several detriments to rice production in Asia. Climate variability has induced flood and drought, which have decreased the rice yield in South Asia and several other parts of Asia (Mottaleb et al., 2017). Heat stress, drought, flood, and cyclones have reduced the rice yield in South Asia (Cai et al., 2016; Quyen et al., 2018; Tariq et al., 2018). Thus, climate change-driven extremes, particularly heat and drought stress, have also become a serious threat for sustainable rice production globally (Xu et al., 2021). Higher temperatures for a longer period as well as water shortages reduce seed germination which lead to poor stand establishment and seedling vigor (Fahad et al., 2017; Liu et al., 2019). It has been reported that the exposure of rice crops to high temperatures (38°C day/30°C night) at the grain filling stage led to a reduction in grain weight of rice (Shi et al., 2017). Moreover, heat stress also reduces the panicle and spikelet's initiation and ultimately the number of spikelets and grains in the rice production system (Xu et al., 2020). Drought stress also adversely affects the reproductive stages and reduces the yield components especially spikelets per panicle, grain size, and grain weight of rice (Raman et al., 2012; Kumar et al., 2020; Sohag et al., 2020). GLAM-Rice model has projected rice yield will decrease ~45% in the 2080's under RCP 8.5 as compared to 1991-2000 in Southeast Asia (Chun et al., 2016). On the other hand, climate variability could reduce crop water productivity by 32% under RCP 4.5, or 29% under RCP 8.5 by 2080's in rice crops (Boonwichai et al., 2019). In China and Pakistan, high temperature adversely affects the booting and anthesis growth stages of rice ultimately resulting in yield reduction (Zafar et al., 2018; Nasir et al., 2020). Crop models like DSSAT and APSIM have projected a yield reduction of both rice and wheat crops up to 19 and 12% respectively by 2069 due to a rise of 2.8°C in maximum and 2.2°C in minimum temperature in Pakistan (Ahmad et al., 2019).

About 35 million farmers having 3% landholding are projected to convert their source of income (combined crop-livestock production systems) to simply livestock because of the negative impacts of climate change on the quality and quantity of pastures as predicted by future scenarios for 2050 in Asia (Thornton and Herrero, 2010). The livestock production sector also contributes 14.5% of global greenhouse emissions and drives climate variability (Downing et al., 2017). Directly, there would be higher disease infestation and reduced milk production and fertility rates in livestock because of climate extremes like heat waves (Das, 2018; Kumar et al., 2018). Indirectly, heat stress will reduce both the quantity and quality of available forage for livestock. Several studies have reported that heat stress reduces the protein and starch content in the grains of maize which is a widely used forage crop (Yang et al., 2018; Bheemanahalli et al., 2022). Similarly, heat stress also reduces the soluble sugar and protein content in the heat-sensitive cultivars of alfalfa which is also a major forage crop (Wassie et al., 2019). In this context, heat stress leads to a reduction in the quality of forage. There would be an increase in demand for livestock products, however, there would be a decrease in livestock heads under future climate scenarios (Downing et al., 2017). In Asia, a severe shortage of feed for livestock has imposed horrible effects on the livestock population which has been attributed as the result of extreme rainfall variability and drought conditions (Ma et al., 2018).

Timber forests have several significances in Asia, and non-timber forests are also significant sources of food, fiber, and medicines (Chitale et al., 2018). Unfortunately, climate change has imposed several negative impacts on forests at various levels in the form of productive traits, depletion of soil resources, carbon dynamics, and vegetation shifting in Asian countries. In India, forests are providing various services in terms of meeting the food demand of 300 million people, the energy demand of people living in rural areas up to 40%, and shelter to one-third of animals (Jhariya et al., 2019). In Bangladesh, forests are also vulnerable to climate variability as they are facing the increased risks of fires, rise in sea level, storm surges, coastal erosion, and landslides (Chow et al., 2019). Increased extreme drought events with higher frequency, intensity, and duration, and human activities, i.e., afforestation and deforestation, have adversely altered the forest structure (Xu et al., 2018). Hence, there is a need to evaluate climate adaptation strategies to restore forests in Asian countries in order to meet increased demands of food, fiber, and medicines. Agroforestry production is also under threat because of adverse climate change impacts such as depletion of natural resources, predominance of insect pests, diseases and unwanted species, increased damage on agriculture and forests, and enhanced food insecurity (De Zoysa and Inoue, 2014; Lima et al., 2022).

Asia also consists of good quality aquaculture (80% of aquaculture production worldwide) and fisheries (52% of wild caught fish worldwide) which are 77% of the total value addition (Nguyen, 2015; Suryadi, 2020). In Asia, various climatic extremes such as erratic rainfall, drought, floods, heat stress, salinity, cyclone, ocean acidification, and increased sea level have negatively affected aquaculture (Ahmad et al., 2019). For instance, Hilsailisha constituted the largest fishery in Bangladesh, India, and West Bengal and S. Yangi in China have lost their habitat because of climate variability (Jahan et al., 2017; Wang et al., 2019a). Ocean acidification and warming of 1.5°C was closely associated with anthropogenic absorption of CO ₂. Increasing levels of ocean acidity is the main threat to algae and fish. Among various climate driven extremes like drought, flood, and temperature rising, drought is more dangerous as there is not sufficient rainfall especially for aquaculture (Adhikari et al., 2018). Similarly, erratic rainfall, irregular rainfall, storms, and temperature variability have posed late maturity in fish for breeding and other various problems (Islam and Haq, 2018).

The above-mentioned facts have indicated that agriculture, livestock, forestry, fishery, and aquaculture are under threat in the future and can drastically affect food security in Asia. This paper reviews the climate change and variability impacts on the cropping system (rice and wheat), livestock, forestry, fishery, and aquaculture and their issues, challenges, and opportunities. The objectives of the study are to: (i) Review the climate variability impacts on agriculture, livestock, forestry, fishery, and aquaculture in Asia; (ii) summarize the opportunities (adaptation and mitigation strategies) to minimize the drastic effects of climate variability in Asia; and (iii) evaluate the impact of climate change on rice-wheat farmer fields-A case study of Pakistan.

Impact of climate change and variability on agricultural productivity

Impact of climate change and variability on rice-wheat crops

In many parts of Asia, a significant reduction in crop productivity is associated with a reduction in timely water and rainfall availability, and erratic and intense rainfall patterns during the last decades (Hussain et al., 2018; Aryal et al., 2019). Despite the increased crop production owing to the green revolution, there is a big challenge to sustain production and improve food security for poor rural populations in Asia under climate change scenarios (FAO, 2015; Ahmad et al., 2019). In the least developed countries, damage because of climactic changes may threaten food security and national economic productivity (Myers et al., 2017). Yield reductions in different crops (rice, wheat) varied within regions due to variations in climate patterns (Yu et al., 2018). CO ₂ fertilization can increase crop productivity and balance the drastic effects of higher temperature in C ₃ plants (Obermeier et al., 2017) but cannot reduce the effect of elevated temperature (Arunrat et al., 2018). Crop growth and development have been negatively influenced because of rising temperatures and rainfall variability (Rezaei et al., 2018; Asseng et al., 2019).

Rice and wheat are major contributors to food security in Asia. There is a big challenge to increase wheat production by 60% by 2050 to meet ever-enhancing food demands (Rezaei et al., 2018). In arid to semi-arid regions, declined crop productivity is attributed to an increase in temperature at lower latitudes. In China, drought and flood have reduced the rice, wheat, and maize yields and it is projected that these issues will affect crop productivity more significantly in the future (Chen et al., 2018). Rice is sensitive to a gradual rise in night temperature causing yield and biomass to reduce by 16-52% if the temperature increase is 2°C above the critical temperature of 24°C (Yang et al., 2017). In Asia, semi-arid to arid regions are under threat and are already facing the problem of drought stress and low productivity. The quality of wheat produce (protein content, sugars, and starch) and grain yield have reduced because of the negative impacts of increasing temperature and erratic rainfall with high intensity (Yang et al., 2017). In the Egyptian North Nile Delta (up to 17.6%), India, and China, the climate variability has decreased wheat yield significantly which is attributed to a rise in temperature, erratic rainfall and increasing insect pest infestation (Arunrat et al., 2018; Shah et al., 2018; Aryal et al., 2019; Kheir et al., 2019). In South Asia, rice yield in rain-fed areas has already decreased and it might reduce by 14% under the RCP 4.5 scenario while 10% under the RCP 8.5 scenario by 2080 (Chun et al., 2016). High temperature and drought have decreased the rice yield because of their adverse impacts on the booting and anthesis stage in Asia, especially in Pakistan and China (Zafar et al., 2018; Ahmad et al., 2019). Similarly, heat stress is a major threat to rice as it decreases the productive tillers, shrinkage of grains, and ultimately grain yield of rice (Wang et al., 2019b). In Asia, climate change would affect upland rice (10 m ha) and rain-fed lowland rice (>13 million hectares). The projected production of rice and wheat crops by 2030 is presented in Table 1 .

Impact of climate change and variability on livestock

In arid to semi-arid regions, the livestock sector is highly susceptible to increased temperature and reduced precipitation (Downing et al., 2017; Balamurugan et al., 2018). A temperature range of 10-30°C is comfortable for domestic livestock with a 3-5% reduction in animal feed intake with each degree rise in temperature. Similarly, the lower temperature would increase the requirement feed up to 59%. Moreover, drought and heat stress would drastically affect livestock production under climate change scenarios (Habeeb et al., 2018). Climate variability affects the occurrence and transmission of several diseases in livestock. For instance, Rift Valley Fever (RVF) due to an increase in precipitation, and tick-borne diseases (TBDs) due to a rise in temperature, have become epidemics for sheep, goats, cattle, buffalo, and camels (Bett et al., 2019). Different breeds of livestock show different responses to higher temperature and scarcity of water. In India, thermal stress has negative impacts on the reproduction traits of animals and ultimately poor growth and high mortality rates of poultry (Balamurugan et al., 2018; Chen et al., 2021; van Wettere et al., 2021). In dry regions of Asia, extreme variability in rainfall and drought stress would cause severe feed scarcity (Arunrat et al., 2018). It has been revealed that a high concentration of CO ₂ reduces the quality of fodder like the reduction in protein, iron, zinc, and vitamins B1, B2, B5, and B9 (Ebi and Loladze, 2019). Future climate scenarios show that the pastures, grasslands, feedstuff quality and quantity, as well as biodiversity would be highly affected. Livestock productivity under future climate scenarios would affect the sustainability of rangelands, their carrying capacity and ecosystem buffering capacity, and grazing management, as well as the alteration in feed choice and emission of greenhouse gases (Nguyen et al., 2019).

Impact of climate change on forest

Climate variability has posed several negative impacts on forests including variations in productive traits, carbon dynamics, and vegetation shift, as well as the exhaustion of soil resources along with drought and heat stress in South Asian countries (Jhariya et al., 2019; Zhu et al., 2021). In Bangladesh, forests are vulnerable to climate variability due to increased risks of fires, rise in sea level, storm surges, coastal erosion and landslides, and ultimately reduction in forest area (Chow et al., 2019). Biodiversity protection, carbon sequestration, food, fiber, improvement in water quality, and medicinal products are considered major facilities provided by forests (Chitale et al., 2018). In contrast, trait-climate relationships and environmental conditions have drastically influenced structure, distribution, and forest ecology (Keenan, 2015). Higher rates of tree mortality and die-off have been induced in forest trees because of high temperature and often-dry events (Allen et al., 2015; Greenwood et al., 2017; Zhu et al., 2021). For instance, trees Sal, pine trees, and Garjan have been threatened by climate-driven continuing forest clearing, habitat alteration, and drought in South Asian countries (Wang et al., 2019). An increase in temperature and CO ₂ fertilization has increased insect pest infestation for forest trees in North China (Bao et al., 2019). As rising temperature, elevated carbon dioxide (CO ₂), and fluctuating precipitating patterns lead to the rapid development of insect pests and ultimately more progeny will attack forest trees (Raza et al., 2015). Hence, there is a need to develop adaptation strategies to restore forests to meet the increasing demand for food, fiber, and medicines in Asia.

Impact of climate change on aquaculture and fisheries

There is a vast difference in response to climate change scenarios of aquaculture in comparison to terrestrial agriculture due to greater control levels over the production environment under terrestrial agriculture (Ottaviani et al., 2017; Southgate and Lucas, 2019). Climatic-driven extremes such as drought, flood, cyclones, global warming, ocean acidification, irregular and erratic rainfall, salinity, and sea level rise have negatively affected aquaculture in South Asia (Islam and Haq, 2018; Ahmad et al., 2019). In Asia, various species such as Hilsa and algae have lost their habitats due to ocean acidification and temperature rise (Jahan et al., 2017). Increased water temperature and acidification of terrestrial agriculture have become dangerous for coral reefs and an increase in average temperature by 1°C for four successive weeks can cause bleaching of coral reefs in India and other parts of Asia (Hilmi et al., 2019; Lam et al., 2019). Ocean warming has caused severe damage to China's marine fisheries (Liang et al., 2018). In Pakistan, aquaculture and fisheries have lost their habitat quality, especially fish breeding grounds because of high cyclonic activity, sea level rise, temperature variability, and increased invasion of saline water near Indus Delta (Ali et al., 2019). It is revealed that freshwater and brackish aquaculture is susceptible to the negative effects of climate variability in several countries of Asia (Handisyde et al., 2017). It is also evaluated that extreme climate variability has deep impacts on wetlands and ultimately aquaculture in India (Sarkar and Borah, 2018).

Climate variability and change impact assessment

Agriculture has a complex structure and interactions with different components, which will make it uncertain in a future climate that is a serious risk to food security in the region. Consequently, it is essential to assess the negative impacts of climate change on agricultural productivity and develop adaptive strategies to combat climate change. Simulation models such as General Circulation Models (GCMs) and Representative Concentration Pathways (RCPs) are being used worldwide for the quantification of the negative effects of climate change on agriculture and are supporting the generation of future weather data (Rahman et al., 2018). Primary tools are also available that can estimate the negative impacts of changing climate on crop productivity, crucial for both availability and access to food. Crop models have the potential to describe the inside processes of crops by considering the temperature rise and elevated CO ₂ at critical crop growth stages (Challinor et al., 2018). There are no advanced methods and technologies available to see the impact of climate variability and change on the production of livestock and crops other than the modeling approach (Asseng et al., 2014). There are also modeling tools available, and being used across the world, to quantify the impacts of climate change and variability on crops and livestock production (Ewert et al., 2015; Hoogenboom et al., 2015; Rahman et al., 2019). We decided to quantify the impacts of future climate on farmer's livelihood to study the complete agricultural system by adopting the comprehensive methodology of climate, crop, and economic modeling (RAPs) approaches and found the agricultural model inter-comparison and improvement project (AgMIP) as the best approach.

A case study-Agricultural model inter-comparison and improvement project

Impact of climate change on the productivity of rice and wheat crops

Department for International Development (DFID) developed the Agricultural Model Inter-comparison and Improvement Project (Rosenzweig et al., 2013) which is an international collaborative effort to deeply investigate the influences of climate variability and change on crops' productivity in different cropping zones/systems across the world and in Pakistan. The mission of AgMIP is to improve the scientific capabilities for assessing the impact of climate variability on the agricultural production system and develop site-specific adaptation strategies to ensure food security at local to global scales. The review discussed above indicated that the agriculture sector is the most vulnerable due to climatic variability and change. Crop production is under threat in Asian countries-predominantly in developing countries. For instance, Pakistan is also highly vulnerable due to its geographical location with arid to semi-arid environmental conditions (Nasi et al., 2018; Ullah et al., 2019; Ghaffar et al., 2022). There would be impacts that are more adverse in arid and semi-arid regions in comparison to humid regions because of climate change and variability (Nasi et al., 2018; Ali et al., 2019). Future climate scenarios have uncertainty and the projected scenario of climate, especially precipitation, did not coincide with the production technology of crops (Rahman et al., 2018). Floods and drought are anticipated more due to variations in rainfall patterns, and dry seasons are expected to get drier in future. Developing regions of the globe are more sensitive to climate variability and change as these regions implement old technologies whereas developed regions can mediate climate-driven extremes through the implementation of modern technologies (Lybbert and Sumner, 2012). The extent of climate change and variability hazards in Pakistan is massive and may be further shocking in the future. Therefore, it is a matter of time to compute climate variability, impacts on crop production, and develop sustainable adaptation strategies to cope with the negative impact of climate change using AgMIP standards and protocols (AgMIP). The main objective is to formulate adaptation strategies to contradict potential climate change effects and support the livelihood of smallholder farmers in the identified area and circulate this particular information to farmers, extension workers, and policy-makers. Sialkot, Sheikhupura, Nankana sahib, Hafizabad, and Gujranwala are considered the hub of the rice-wheat cropping system (Ghaffar et al., 2022), with an area of 1.1 million hectares. The rice-wheat cropping system is a food basket and its sustainable productivity in future climates will ensure food security in the country and generally overall in the region.

Methodology of the case study

Field data collection

Field data included the experimental trials and socio-economic data of 155 successive farmers' farms collected during an extensive survey of rice-wheat cropping zone from five-selected districts ( Figure 5 ). From each district, randomly two villages were selected from each division, randomly 30 respondents and 15 farms of true representation of the farming population from each village considered. Crop management data included all agronomic practices from sowing to harvesting such as planting time, planting density, fertilizers amount and organic matter amendment, irrigation amount and intervals, cultural operations, grain yield, and biomass production collected for both crops, rice and wheat, and overall, for all systems. Farm data for the rice-wheat cropping system were analyzed with crop and economic models to see the impact of climate variability on crop production.

Historic and future climatic data

Daily historic data was collected from the Pakistan Meteorological Department (PMD) for all study locations. The quality of observed weather data was checked following the protocol of the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols (AgMIP, 2013). Station-based downscaling was performed with historic weather data from all study sites/locations in the rice-wheat cropping zone. For the zone/region, five GCMs (CCSM4, GFDL-ESM2M, MIROC5, HadGEM2-ES, and MPI-ESM-MR) of the latest CMIP5 family were engaged for the generation of climate projections for the mid-century period using the RCP 8.5 concentration scenario, and using the protocols and methodology developed by AgMIP (Ruane et al., 2013, 2015; Rahman et al., 2018). GCMs were selected on the basis of different factors such as better performance in monsoon seasons, the record of accomplishment of publications, and the status of the model-developing institute. Under the RCP 8.5 scenario, an indication of warming ranges 2-3°C might be expected in all selected districts for the five CMIP5, GCMs in comparison to the baseline between the periods of 2040-2069. However, there is no uniform warming recorded under all 5 CMIP5 GCMs. For instance, CCSM4 and GFDL-ESM-2M showed uniform increased temperatures during April and September months. The outputs of the GCMs indicated large variability in the estimated values of precipitation. The HadGEM2-ES and GFDL-ESM2M projected mean of 200 and 100 mm between times 2040-2069, respectively. On average, a minor rise in annual rainfall (mm) is indicated by five GCMs in comparison to the baseline.

Crop models (DSSAT and APSIM)

To understand the agronomic practices and the impact of climate variability on the development and growth of plants, crop simulation models like DSSATv4.6 (Hoogenboom et al., 2015, 2019) and APSIMv7.5 (Keating et al., 2003) were applied. Three field trials were conducted on rice and wheat crops during two growing seasons, to collect the data like phenology, crop growth (leaf area index, biomass accumulation), development, yield, and agronomic management data by following the standard procedure and protocols. Crop models are calibrated with experimental field data (phenology, growth, and yield data) under local environmental conditions by using soil and weather data. Crop models were further validated with farmers' field data of rice and wheat crops. Climate variability impact on both crops was assessed with historic data (baseline) and future climate data of mid-century in this region.

Tradeoff analysis model for multi-dimensional impact assessment

For the analysis of climate change impact socio-economic indicators, version 6.0.1 of the Tradeoff Analysis Model for Multi-Dimensional Impact Assessment (TOA-MD) Beta was employed (Antle, 2011; Antle et al., 2014). It is an economical and standard model employed for the analysis of technology adoption impact assessment and ecosystem services. Schematically illustrated, showing connections between the different models and the points of contact between them in terms of input-output in a different climate, crop and economic models and climate analysis is shown in Figure 6 . Various factors that may affect the anticipated values of the production system are technology, physical environment, social environment, and representative agricultural pathways (RAPs), hence it is necessary to distinguish these factors (Rosenzweig et al., 2013). RAPs are the qualitative storylines that can be translated into model parameters such as farm and household size, practices, policy, and production costs. For climate impact assessment, the dimensionality of the analysis is the main threat in scenario design. Farmers employ different systems for operating a base technology. For instance, system 1 included base climate, in system 2, farmers use hybrid climate, and in system 3, farmers use perturbed climate to cope with future climate with adaptation technology. The analysis gave the answer to three core questions (Rosenzweig et al., 2013). First, without the application RAPs of the core question, one-climate change impact assessments (CC-IA) were formulated. Second, analysis was again executed for examining the negative effects of climate change on future production systems. Third, analysis was executed for future adapted production systems through RAPs and adaptations. Two crop models, i.e., DSSAT and APSIM, outputs were used as the inputs of TOA-MD. Different statistical analyses like root mean square error (RMSE), mean percentage difference (MPD) d-stat, percent difference (PD), and coefficient of determination (R2) were used to check the accuracy of models.

Results

Farmers field data validation

Crop model simulation results regarding calibration and validation of both crops (rice and wheat) were in good agreement with the field experimental data. Both models were further validated using farmers' field data of rice and wheat crops in rice-wheat cropping zone after getting robust genetic coefficients. Model validation results of 155 farmers of rice and wheat crops indicated the good accuracy of both models (DSSAT, APSIM) and have a good range of statistical indices. Both of these crop models showed an improved ratio between projected and observed rice yield in farmers' fields with RMSE 409 and 440 kg ha −1 and d-stat 0.80 and 0.78, respectively. Similarly, the performance of models DSSAT and APSIM for a yield of wheat was also predicted with RMSE of 436 and 592 kg ha −1 and d-stat of 0.87, respectively.

Quantification of climate change impact by crop models

Climate change impact assessment results in the rice-wheat cropping zone of 155 farms indicated that yield reduction varied due to differences in GCM's behavior and variability in climatic patterns. It is predicted that mean rice yield reduction would be up to 15 and 17% for DSSAT and APSIM respectively during mid-century while yield reduction variation among GCMs are presented in Figure 7 . Rice indicated a yield decline ranging from 14.5 to 19.3% for the case of APSIM while mean yield reduction of the rice crop was between 8 and 30% with DSSAT. Reduction in production of wheat varied among GCMs as well as an overall reduction in yield in rice-wheat cropping systems. For wheat, with DSSAT would be a 14% reduction whereas for APSIM, the reduction would be 12%. GCMs reduction in wheat yield for midcentury (2040-2069) is shown in Figure 8 . Reduction in wheat yield for all 5 GCMs was from 10.6 to 12.3% in the case of APSIM while mean reduction in wheat yield was between 6.2 and 19%. As rice is a summer crop where the temperature is already high and, according to climate change scenarios, there is an increase in both maximum and minimum temperature, an increase in minimum temperature leads to more reduction in yield as compared to wheat being a winter season crop. It was hypothesized that the increase in night temperature (minimum temperature) leading to more losses in the summer season may be due to high temperature, particularly at anthesis and grain formation stages in rice crops, as it is already an irrigated crop and rainfall variability (more rainfall) cannot reduce the effect of high temperature in the rice yield as compared to the wheat crop.

Climate change economic impact assessment and adaptations

Sensitivity of current agricultural production systems to climate change

Climate change is damaging the present vulnerabilities of poor small farmers as their livelihood depends directly on agriculture. Noting various impacts of future climate (2040-2069) on a current production system (current technologies), we examine the vulnerability of the current production system used for the assessment of the adverse impacts of climate change on crop productivity and other socio-economic factors. Climate change impacts possible outcomes for five GCMs based on the estimation of yield generated by two crop models presented in Table 2 . In Table 3 , and the grain losses and net impacts as a percentage of average net returns for the first core question are given for each GCM. The analysis clearly shows the observed values of the mean yield of wheat and rice, which are estimated to be 18,915 kg and 18,349 kg/ farm respectively in the projected area. For all GCMs, observed average milk production was 3,267 liters per farm with a 12% average decline in yield found under livestock production. Losses were about 69-83% and from 72 to 76% for DSSAT and APSIM respectively as predicted by TOA-MD analysis because of the adverse effects of climate change situations. For DSSAT, percentage losses and gains in average net farm returns were from 13 to 15% and 23 to 30%, respectively. While gains were 14-15% and losses were from 25 to 27%, respectively for APSIM. Without adverse impacts of climate change, a net income of Rs. 0.54 per farm pragmatic was predicted by DSSAT and APSIM. However, DSSAT predicted Rs. 0.42-0.48 M per farm and APSIM predicted Rs. 0.45-0.47 M net income per farm under climate change for all GCMs. An increase in the poverty rate in climate change situations would be 33-38% for DSSAT and it would be 35-37% for APSIM, respectively while the rate of poverty with no adverse impacts of climate change would be 29%.

Table 2

Time averaged relative yield (r) of GCM

Crop

Crop Model

CCSM4

GFDL-ESM2M

HadGEM2-ES

MIROC5

MPI-ESM-MR

Mean

Table 3

Crop model

Poverty rate (%) with climate change

Losses (%)

Gains (% mean net returns)

Losses (% mean net returns)

Net impacts (% mean net returns)

DSSAT

16.6

57.0

13.2

15.6

−2.4

APSIM

19.1

63.2

13.4

18.5

−5.1

Impacts of climate change on future agricultural production systems

In regard to the second core question, a comparison of system 1 (current climate and future production system) with system 2 (future climate and future production system in mid-century) was analyzed with the aid of TOA-MD using 5 GCMs. Mean wheat and rice yield reduction for DSSAT was from 6.2 to 19% and 8 to 30% respectively, and APSIM indicated a decline ranging from 10.6 to 12.3% and 14 to 19%, respectively. For all analyses of Q2, the projected mean yield was 25,073 kg per farm under rice production. While in the case of livestock for all analyses, the mean projected milk production was 3,267 L/farm with its mean decline in yield estimated to be about 12%. Percentage losses for DSSAT and APSIM would fluctuate between 57 and 70% and from 61 to 71%, respectively for all five GCMs.

Mean net farm returns for gains and losses, as a percentage for DSSAT would be 11-13% and from −16 to −22%, respectively. While the percentage of gains and losses would be between 10 and 15% and −17% and −19% in the case of APSIM, respectively. DSSAT predicted Rs. 89-100 thousand per person while APSIM predicted Rs. 93-97 thousand per person per capita income in changing climatic scenarios. For both crop models, the poverty rate will be 16% without climate change. While poverty rates will be from 17 to 19% in the case of DSSAT and ranging from 18 to 19% for APSIM with climate change ( Table 3 ).

Evaluation of potential adaptation strategies and representative agricultural pathways

Adaptation technologies for rice and wheat crops ( Table 4 ) are used in crop growth models and economic TOA-MD model analysis ( Table 5 ) for simulating the sound effects of prospective adaptation strategies on both adapters and non-adapters distribution. This TOA-MD analysis compared "system 1" (incorporating RAPs) and "system 2" (incorporating RAPs and adapted technology) for the rice-wheat system in the mid-century based on crop models DSSAT and APSIM using 5 GCMs. The mean yield change of wheat and rice crops was from 60 to 72% for DSSAT and 70 to 80% for APSIM respectively, wheat crop indicated a change that ranges from 80 to 89% and 62 to 84% for all five GCMs ( Figure 9 ). Under livestock production, the estimated average production of milk exclusive of adaptation was 3,593 liters/farm for all analyses and for all cases indicates a 42% increase in average yield. The percentage of adopters due to adaptation technologies for DSSAT and APSIM in rice-wheat cropping systems would be between 92 and 93% and 93 and 94%, respectively. For DSSAT and APSIM estimated per head income with adaptation cases will be from Rs. 89 to 100 and 93 to 97 thousand and from Rs. 156 to 174 and 166 to 181 thousand per head, respectively in a year. Without and with adaptation, poverty would range between 17 and 19% and 12 and 13% respectively, for DSSAT and from 18 to 19% and 12 to 13%, respectively for APSIM ( Table 6 ). Climatic changes in the rice-wheat cropping areas of Punjab province will have less impact on the future systems after implementing the adaptation strategies, with a large and significant impact imposed by these adaptations.

Table 4

Sr. #

Variable

Direction of change

% change

Rice

Wheat

Table 5

Sr. #

Variable

Direction of change

% change

Rice

Wheat

Table 6

Economic indicators

DSSAT

APSIM

Projected adoption rate = 69.3%

Projected adoption rate = 70%

Without adaptation

With adaptation

Without adaptation

With adaptation

Discussion

Opportunities in the era of climate change for agriculture

Scope of adaptation and mitigation strategies for sustainable agricultural production

It is essential to assess the impact of climate variability on agricultural productivity and develop adaptation strategies/technology to cope with the negative effects to ensure sustainable production. The hazardous climate change effects can be reduced by adapting climate-smart and resilient agricultural practices, which will ensure food security and sustainable agricultural production (Zafar et al., 2018; Ahmad et al., 2019; Ahmed et al., 2019b). Adaptation is the best way to handle climate variability and change as it has the potential to minimize hazardous climate change effects for sustainable production (IPCC, 2019). Innovative technologies and defensive adaptation can reduce the uncertain and harmful effects of climate on agricultural productivity.

Therefore, to survive the harmful climate change effects, the development and implementation of adaptation strategies are crucial. In developing countries, poverty, food insecurity and declined agricultural productivity are common issues, which indicate the need for mitigation and adaptation measures to sustain productivity (Clair and Lynch, 2010; Lybbert and Sumner, 2012; Mbow et al., 2014). At the national and regional level, the insurance of food security is the major criterion for the effectiveness of mitigation and adaptation. Integration of adaptation and mitigation strategies is a great challenge to promote sustainability and productivity. Climate resilient agricultural production systems can be developed and diversified with the integration of land, water, forest biodiversity, livestock, and aquaculture (Hanjra and Qureshi, 2010; Meena et al., 2019). Summary and overview of all below discussed potential opportunities are presented in Figure 10 .

Reduction in GHGs emission

Reduction in GHGs emissions from agriculture under marginal conditions and production of more food are the major challenges for the development of adaptation and mitigation measures (Smith and Olesen, 2010; Garnett, 2011; Fujimori et al., 2021). Similarly, it is an immediate need to control such practices in agriculture which lead to GHGs emissions, i.e., N ₂O emissions from the application of chemical fertilizers, and CH ₄ emissions from livestock and rice production systems (Herrero et al., 2016; Allen et al., 2020). Similarly, alternate wetting and drying and rice intensification are important to reduce the GHGs emission from rice crops (Nasir et al., 2020). Carbon can be restored in soil by minimizing the tillage, reducing soil erosions, managing the acidity of the soil, and implementing crop rotation. By increasing grazing duration and rotational grazing of pastureland, sequestration of carbon can be achieved (Runkle et al., 2018). About 0.15 gigatonnes of CO ₂ equal to the amount of CO ₂ produced in 1 year globally, can be sequestered by adopting appropriate grazing measures (Henderson et al., 2015). Development of climate-resilient breeds of animals and plants with higher growth rates and lower GHGs emissions should be developed to survive under harsh climatic conditions. Focus further on innovative research and development for the development of climate-resilient breeds, especially for livestock (Thornton and Herrero, 2010; Henry et al., 2012; Phand and Pankaj, 2021).

Application of ICT and decision support system

To mitigate and adapt to the drastic effects of climate variability and change, information and communication technologies (ICTs) can also play a significant role by promoting green technologies and less energy-consuming technology (Zanamwe and Okunoye, 2013; Shafiq et al., 2014; Nizam et al., 2020). Timely provision of information from early warning systems (EWS) and automatic weather stations (AWS) on drought, floods, seasonal variability, and changing rainfall patterns can provide early warning about natural disasters and preventive measures (Meera et al., 2012; Imam et al., 2017), and it can also support farmers' efforts to minimize harmful effects on the ecosystems. Geographical information systems (GIS), wireless sensor networks (WSN), mobile technology (MT), web-based applications, satellite technology and UAV can be used to mitigate and adapt to the adverse effects of climate change (Kalas, 2009; Karanasios, 2011). Application of different climate, crop, and economic models may also help reduce the adverse effects of climate variability and change on crop production (Hoogenboom et al., 2011, 2015, 2019; Ewert et al., 2015).

Crop management and cropping system adaptations

Adaptation strategies have the potential to minimize the negative effect of climate variability by conserving water through changes in irrigation amount, timely application of irrigation water, and reliable water harvesting and conservation techniques (Zanamwe and Okunoye, 2013; Paricha et al., 2017). Crop-specific management practices like altering the sowing times (Meena et al., 2019), crop rotation, intercropping (Hassen et al., 2017; Moreira et al., 2018), and crop diversification and intensification have a significant positive contribution as adaptation strategies (Hisano et al., 2018; Degani et al., 2019). Meanwhile, replacement of fossil fuels by introducing new energy crops for sustainable production (Ruane et al., 2013) is also crucial for the sustainability of the system. Different kinds of adaptation actions (soil, water, and crop conservation, and well farm management) should be adapted in case of long-term increasing climate change and variability (Williams et al., 2019). Similarly, alteration in input use, changing fertilizer rates for increasing the quantity and quality of the produce, and introduction of drought resistant cultivars are some of the crucial adaptation approaches for sustainable production. Therefore, under uncertain environmental conditions, to ensure sustainable productivity, crops having climatic resilient genetic traits should also be introduced (Bailey-Serres et al., 2018; Raman et al., 2019). Similarly, to ensure the sound livelihood of farmers, it is important to develop resilient crop management as well as risk mitigation strategies.

Opportunities for a sustainable livestock production system

The integration of crop production, rearing of livestock and combined use of rice fields for both rice and fish production lead to enhancing the farmers' income through diversified farming (Alexander et al., 2018; Poonam et al., 2019). Similarly, variations in pasture rates and their rotation, alteration in grazing times, animal and forage species variation, and combination production of both crops and livestock are the activities related to livestock adaptation strategies (Kurukulasuriya and Rosenthal, 2003; Havlik et al., 2013). Under changing climate scenarios, sustainable production of livestock should coincide with supplementary feeds, management of livestock with a balanced diet, improved waste management methods, and integration with agroforestry (Thornton and Herrero, 2010; Renaudeau et al., 2012).

Carbon sequestration and soil management

Selection of more drought-resilient genotypes and combined plantation of hardwood and softwood species (Douglas-fir to species) are considered adaptive changes in forest management under future climate change scenarios (Kolstrom et al., 2011; Hashida and Lewis, 2019). Similarly, timber growth and harvesting patterns should be linked with rotation periods, and plantation in landscape patterns to reduce shifting and fire of forest tree species under climate-smart conditions for forest management to increase rural families' income for a sustainable agricultural ecosystem (Scherr et al., 2012). Although, conventional mitigation methods for the agriculture sector have a pivotal role in forest related strategies, some important measures are also included in which afforestation and reforestation should be increased but degradation and deforestation should be reduced and carbon sequestration can be increased (Spittlehouse, 2005; Seddon et al., 2018; Arehart et al., 2021). Carbon stock enhanced the carbon density of forest and wood products through longer rotation lengths and sustainable forest management (Rana et al., 2017; Sangareswari et al., 2018). Climate change impacts are reduced through adaptation strategies in agroforestry including tree cover outside the forests, increasing forest carbon stocks, conserving biodiversity, and reducing risks by maintaining soil health sustainability (Mbow et al., 2014; Dubey et al., 2019). Similarly, climate-smart soil management practices like reduction in grazing intensity, rotation-wise grazing, the inclusion of cover and legumes crops, agroforestry and conservation tillage, and organic amendments should also be promoted to enhance the carbon and nitrogen stocks in soil (Lal, 2007; Pineiro et al., 2010; Xiong et al., 2016; Garcia-Franco et al., 2018).

Opportunities for fisheries and aquaculture

Sustainable economic productivity of fisheries and aquaculture requires the adaptation of specific strategies, which leads to minimizing the risks at a small scale (Hanich et al., 2018). Therefore, to build up the adaptive capacity of poor rural farmers, measures should be carried out by identifying those areas where local production gets a positive response from variations in climatic conditions (Dagar and Minhas, 2016; Karmakar et al., 2018). Meanwhile, the need to build the climate-smart capacity of rural populations and other regions to mitigate the harmful impacts of climate change should be recognized. In areas which have flooded conditions and surplus water, the integration of aquaculture with agriculture in these areas provides greater advantages to saline soils through newly adapted aquaculture strategies, i.e, agroforestry (Ahmed et al., 2014; Dagar and Yadav, 2017; Suryadi, 2020). To enhance the food security and living standards of poor rural families, aquaculture and artificial stocking engage the water storage and irrigation structure (Prein, 2002; Ogello et al., 2013). In Asia, rice productivity is increased by providing nutrients by adapting rice-fish culture in which fish concertedly consume the rice stem borer (Poonam et al., 2019). Food productivity can be enhanced by the integration of pond fish culture with crop-livestock systems because it includes the utilization of residues from different systems (Prein, 2002; Ahmed et al., 2014; Dagar and Yadav, 2017; Garlock et al., 2022). It is important to compete with future challenges in the system by developing new strains which withstand high levels of salinity and poorer quality of water (Kataria and Verma, 2018; Lam et al., 2019).

Conclusion

Globally, and particularly in developing nations, variability in climatic patterns due to increased anthropogenic activity has become clear. Asia may face many problems because of changing climate, particularly in South Asian countries due to greater population, geographical location, and undeveloped technologies. The increased seasonal temperature would affect agricultural productivity adversely. Crop growth models with the assistance of climatic and economic models are helpful tools to predict climate change impacts and to formulate adaptation strategies. To respond to the adverse effects of climate change, sustainable productivity under climate-smart and resilient agriculture would be achieved by developing adaptation and mitigation strategies. AgMIP-Pakistan is a good specimen of climate-smart agriculture that would ensure crop productivity in changing climate. It is a multi-disciplinary plan of study for climate change impact assessment and development of the site and crop-specific adaptation technology to ensure food security. Adaptation technology, by modifications in crop management like sowing time and density, and nitrogen and irrigation application has the potential to enhance the overall productivity and profitability under climate change scenarios. The adaptive technology of the rice-wheat cropping system can be implemented in other regions in Asia with similar environmental conditions for sustainable crop production to ensure food security. Early warning systems and trans-disciplinary research across countries are needed to alleviate the harmful effects of climate change in vulnerable regions of Asia. Opportunities as discussed have the potential to minimize the negative effect of climate variability and change. This may include the promotion of agroforestry and mixed livestock and cropping systems, climate-smart water, soil, and energy-related technologies, climate resilient breeds for crops and livestock, and carbon sequestration to help enhance production under climate change. Similarly, the application of ICT-based technologies, EWS, AWS, and decision support systems for decision-making, precision water and nutrient management technologies, and crop insurance may be helpful for sustainable production and food security under climate change.

Author contributions

AA, MH-u-R, and AR: conceptualization, validation, and formal analysis. MH-u-R, SAh, AB, WN, AE, HA, KH, AA, FM, YA, and MH: methodology, editing, supervision, and project administration. Initial draft was prepared by MH-u-R and improved and read by all co-authors. All authors contributed to the article and approved the submitted version.

Funding

This research funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia under grant number (IFPRP: 530-130-1442).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors extend their appreciation to Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (IFPRP: 530-130-1442) and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.

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Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia

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Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect ...