One of the problems facing us in the current and future centuries is climate change, which affects the agricultural sector while being affected by climate change. smart climate agriculture is an approach that seeks to adjust agriculture and climate change. On the other hand, smart climate agricultural actions has been introduced to help farmers to improve productivity and also increase the degree of adaptation to climate change and reduce greenhouse gas emissions. The purpose of this research is to accept the farmers of Behbahan city in using this amount of consumption. In order to achieve this goal, a descriptive-all and causal-relational method and a questionnaire tool are used, the validity of which is used using the scientific methods of agricultural jihad of Behbahan city, the faculty of the department of agricultural promotion and education and water resources management of the university. he does. Receipt. To determine the reliability of this tool, it was calculated using Cronbach's alpha coefficient (0.85 ≥ α ≥ 0.70). The statistical population of the study was made up of farmers living in the Maroon watershed in Behbahan city, who are constantly exposed to the climate change phenomenon (N=51838). The number of members of the research sample was determined using the Karjesi and Morgan table of 177 people who were selected by simple random sampling and answered the research questions. In order to analyze the data, SPSS version 26 software was used. The descriptive findings of this research showed that most of the respondents were male, their age was 38.34 years, most of the respondents had higher education. The majority of the respondents only received advice on agricultural jobs in the region, and the majority of them received advice from Jihad Agricultural Service Centers. Three changes in the decision to use technology with a coefficient of 0.648, subjective perception of usefulness with a coefficient of 0.355 and subjective perception of ease with a coefficient of 0.328 respectively had the highest total causal effect on the use of smart climate agricultural technologies.

The low adoption of climate-smart agriculture (CSA) technologies by farmers in developing regions where agrarian livelihoods are threatened by climate-related disasters such as drought remains a concerning enigma. Adoption patterns are not commensurate with merits of CSA on food security and climate resilience and attention to Socio-psychological features in relation to behavioral and attitudinal patterns in CSA adop-tion remains scarce. Accordingly, this research aimed to study adoption of CSA using the extended technology acceptance model in Sistan plain. The study was conducted on the farmers in the Sistan plain in Sistan and Baluchistan province (N = 6000). The sample (361 farmers) was taken by the proportionally allocated stratified random technique. The results revealed that variables of perceived usefulness, perceived ease of use, farmers’ subjective norms, and attitude had significant effects on their intention to apply CSA practices. In addition, farmers’ subjective norms and attitude towards CSA had significant effects on perceived usefulness, perceived ease of use. This shows the critical role of social influences in shaping attitudes and behavior for CSA adoption and could be useful in educating farmers on climate change.

Food and agriculture Organization of the United Nations (FAO) has introduced climate-smart agriculture as a resistant and productive agriculture for better management of resources. This study aimed to recognition and obtaining the affecting or undergoing (cause and effect, and mutual relationship) components of climate-smart agriculture. DEMATEL technique was employed to identify and extract the Mutual relationships (effective or influential, causal and causal relationships) of the components of the climate-smart agriculture and the extent to which they interact with this agricultural system. The research was applied to a descriptive-survey method. The statistical population of the study was 15 experts in agricultural development, Agricultural extension and education, meteorology and soil science, University of Tehran. The research instrument was a questionnaire whose validity (its appearance and content) was confirmed by the opinion of professors of Tehran University. In this research, Excel software and DEMETLIC technique have been used to analyze the data. According to the results of the research, the most effective variable among the main components of agriculture in the climate-smart agricultural, increase in adaptability with effect intensity of 0.34 and the most effective the influence variable is, income sustainability with the effect intensity of 0.44, and also climate-smart agriculture with 11.18 highest interaction (both effect And effect sustainability) and income sustainability with a coefficient of 8.63 has the least interaction with other components and the agricultural system itself. The results indicate that among the main sub-components of agriculture, climate-smart agricultural is the most effective variable, improvement of water management with the intensity of 0.59 and the most effective variable, maintaining land use with the intensity of 0.53, and in terms of interaction, agricultural climate with a coefficient 3.14. The highest and 0.55 percent of agricultural insurance products have the least interaction with other subspecies and the agricultural system itself.

Background: The agricultural sector is a crucial component of a nation's economy, playing an indispensable role in its economic development. This sector not only ensures food security but also provides employment opportunities and contributes to rural development. The integration of technology into agriculture is vital for increasing production, enhancing efficiency, and facilitating access to global markets. Recent advances in emerging technologies have led to a significant paradigm shift within the agricultural landscape. This transformation, driven by digital technologies, is increasingly recognized as a flexible and sustainable solution for modernizing agricultural practices. This research aims to investigate technological trends and their functions in the agricultural value chain, ultimately facilitating a successful transition to smart agriculture. By exploring these trends, the study seeks to highlight the potential benefits of adopting innovative technologies in agriculture, including improved productivity, better resource management, and enhanced sustainability. Methods: This research is practical in its objectives and systematic in its approach to data collection. The statistical population includes all English-language articles published in the field of smart agriculture between 2011 and 2022, sourced from the Web of Science and Scopus databases. In the initial phase, 3,257 articles were identified and analyzed using Vos Viewer for scientometric analysis. This software allows for the visualization of scientific data and the mapping of research trends, providing insights into the evolution of the field. Following this analysis, the research question was formulated, and the Prisma approach was applied to refine the selection of articles. Ultimately, 33 articles were chosen for a comprehensive qualitative study. This combination of quantitative and qualitative methodologies enables a thorough exploration of the topic, offering both statistical insights and in-depth analyses of the selected literature. Results: In the quantitative section of this research, knowledge maps were utilized to illustrate the growth trend of scientific articles in the field of smart agriculture. This analysis included the identification of key journals, research centers, and active countries contributing to this body of work. Furthermore, the study pinpointed authors with the highest publication outputs and assessed scientific networks to evaluate their influence within the field. The results indicate a growing interest in smart agriculture, reflected in the increasing number of publications and collaborations among researchers. In the qualitative section, the research findings reveal that the Internet of Things (IoT) is the most significant technological domain in the agricultural sector. IoT enables the interconnectivity of devices and systems, facilitating real-time data collection and monitoring, which are essential for informed decision-making in agriculture. Other important technologies identified include artificial intelligence, machine learning, big data analytics, blockchain technology, wireless sensor networks, remote sensing, various types of sensors, cloud computing, fog and edge computing, and low-power wide-area networks. These technologies can be categorized into four primary groups based on their functionalities: data collection, computation and communication, data security and integration, and prediction and classification. Data collection technologies facilitate the gathering of agricultural data from various sources, enabling farmers to make data-driven decisions. Computation and communication tools process and transmit data efficiently, ensuring timely access to critical information. Data security and integration solutions protect sensitive agricultural data while integrating various systems for seamless operation. Prediction and classification technologies assist in forecasting agricultural trends and categorizing data for better management practices. The insights generated from this research are expected to serve as a critical foundation for managers and policymakers. Understanding the current technological landscape will enable them to formulate strategies, policies, and development programs that effectively leverage these innovations to enhance productivity and sustainability in the agricultural sector. Conclusion: Based on the identified trends, it is clear that government intervention is essential for fostering the development of smart agriculture. By adopting strategic measures and creating appropriate frameworks, the government can significantly contribute to this sector's advancement. Key actions include developing technical infrastructure, investing in human capital, supporting research and development initiatives, encouraging smart agricultural practices, providing financial support, and establishing guaranteed markets for agricultural products. These efforts will not only enhance productivity in the agricultural sector but also promote sustainable development, ultimately leading to an improvement in the country's capabilities. The transition to smart agriculture represents a significant opportunity to address the challenges faced by the agricultural sector, including climate change, resource scarcity, and the need for increased food production. By embracing technological advancements and fostering an environment conducive to innovation, nations can ensure the long-term viability and competitiveness of their agricultural industries. In summary, the findings of this study underscore the importance of technological integration within the agricultural value chain. As the sector continues to evolve, stakeholders must remain vigilant and adaptive to emerging trends, ensuring that they harness the full potential of technology to drive growth and sustainability. Future research should continue to explore the impacts of these technologies on agricultural practices and investigate additional strategies for enhancing their adoption across diverse agricultural contexts. This comprehensive approach will not only benefit individual farmers but also contribute to the broader economic and social well-being of communities, ultimately paving the way for a more resilient and sustainable agricultural future.

Modern irrigation systems are considered as a way to both respond to the effects of climate changes and improve the water security. Applying such systems, save the water used in farming activities and consequently made some environmental challenges in terms of increasing energy consumption and greenhouse gas emissions. Although some recent studies analyzed the relationship between water and energy in the agricultural irrigation systems, considering the objectives on productivity, adaptation, and mitigation in a cropping pattern optimization problem is necessary. climate-smart agriculture as a strong programming concept, addresses these three objectives and has created the potential for a "triple-win" solution. This study is an effort to fill the study gap on triple-win solution in modern irrigation by developing an integrated economic-hydrological-environmental model called WECSAM at the basin level using a hydrological model called WEAP. For this purpose, a multi-objective optimization model has been developed with the concepts of water footprint, energy footprint, and the greenhouse gas emissions in the context of CSA. We applied the model to the northern region of Bakhtegan basin called Doroodzan irrigation network located in Iran. The result of the WECSAM model indicated that by simultaneously optimizing the conflicting objectives of maximizing profit and minimizing water footprint, energy footprint, and CO2 emissions, as compared to the single-objective model of maximizing economic profit, the water footprint decreases by 8. 2%, Energy footprint decreases by 21. 2%, CO2 emissions decreases by 6. 9% and profit decreases by 7. 4%. The share of each system in irrigating the water-smart, energy-smart, and climate-smart cropping pattern is as follow: 54% for drip system, 26% for semi-permanent sprinkler system, 11% for surface systems, 8% for center-pivot, and <1% for classic permanent sprinkler system.

The purpose of the present research was to analyze the content of the basic technical knowledge textbooks of horticulture, agricultural and livestock affairs of the second cycle secondary school from the perspective of smart climate agriculture. The current research was applied in terms of purpose and in terms of method, it was a descriptive study of content analysis type. To analyze the content and determine the active and inactive categories, the William Rumi technique was used, and the results and data were processed using Shannon's entropy method. The research population consisted of the content of the second cycle secondary school basic technical knowledge textbooks of horticulture, livestock and agricultural affairs published in 1401 academic year. All texts, questions and pictures of the textbooks were checked by census. The data collection tool was a note-taking form and a content analysis checklist based on William Rumi and Shannon's entropy tables. Findings showed that the coefficient of students' engagement with the text of books of basic technical knowledge of horticultural, livestock and agricultural affairs were 0.3, 0.7, and 0.3, respectively. The coefficient of students' engagement with the pictures and questions of the mentioned textbooks were also 0.3, 0.5 and 0.3, respectively. Therefore, according to William Rumi, in terms of the text, pictures and questions of the textbooks, only the textbook of basic technical knowledge of livestock affairs is active. Finally, the results showed that in the text, pictures and questions of the textbooks under study, the information load and the importance coefficient of inactive categories are more than active ones.

In recent years, the Internet of Things (IoT) brings a new dimension for establishing a precision network connectivity of sensors, especially in the agriculture and farming industry, medical, economic, and several sectors of modern society. agriculture is an important area for the sustainability of mankind engulfing manufacturing, security, and resource management. Due to the exponential diminishing of the resources, innovative techniques to support the subsistence of agriculture and farming. IoT aims to extend the use of internet technology to a large number of distributed and connected devices by representing standard and interoperable communication protocols. This paper brings up a solution by IoT, presents the design and implementation of a smart pre-seeding decision support system for agricultural modernization. This project is accomplished by understanding the real-time circumstances in the agriculture field using wireless technology that highlighted the features including pH and temperature sensors, hardware, mobile application, system’ s frontend, and backend analysis, and stores the extracted information in the cloud using IoT. The system is made up of frontend data acquisition, data transmission, data processing, and reception, and is also experimentally validated to find out all possible crops that can be cultivated in a specific land with the required amount of fertilizers as well as the overall crops distribution lists.

Internet of Things (IoT) as an emerging technology in the field of Information and Communication Technology is the next revolution related to the Internet application. IoT focuses on the communication of things such as sensors, drivers, devices, etc., with data collection capability controlling remote communication rather than focusing on the communication between people. Development of smart solutions and new technologies of IoT in agriculture can pave the way to a new paradigm of farming called “ smart agriculture” by making a fundamental change in all aspects of current practices. IoT-based smart agriculture can improve agricultural productivity with more food production through the optimal utilization of the basic resources, minimizing environmental impacts, reducing the costs, and increasing the incomes with linking to the business market that facilitates sustainable agricultural development goals. IoT-based data is a collection of large data called “ Big Data” that cannot be processed and managed by traditional databases and conventional management tools. IoT and Big Data technologies are interconnected and it can be predicted that the future of optimal agriculture in the world would not be possible to meet the food demand and sustainability of production without these technologies and smart agriculture. This article introduces IoT and Big Data technologies, as well as the relationship between them from the vision of smart agriculture. Moreover, the article aims to assist in the decision-making of the strategy from the pre-production stage to the business marketing in the country by assessing life cycle and technology trends. Some of the big IoT data applications in the smart agriculture cycle are also introduced.

There are obvious evidences showing that climate change will affect adversely the agriculture sector especially those of the developing nations in coming decades. Regarding the climate change effects on the agriculture sector; this study aims to examine the impact of climatic variables on the Iranian agriculture value added. To get the study objective, provincial panel data over 2006-2016 was applied. Temperature and precipitation were applied to examine the effect climate change on agriculture value added. The remaining variables affecting agriculture value added are labor, physical capital including land, livestock and irrigation equipment, human capital measured as the number of the university graduated population, social capital represented by phone lines in rural area and trade index. Trade impact was also investigated using Foreign Direct Investment (FDI) and trade openness variables. Based on the results, the climatic variables’ fluctuations or deviations from their average found to have more significant impact. One degree increase in temperature beyond its long term average is expected to induce a reduction of 5% in agriculture production while the corresponding value for 1% reduction in precipitation is 0. 3%. Among the capital variables, physical capital has the highest contribution to agriculture production. Production elasticity with respect to social capital exceeds those of human capital, ranging from 0. 17 to 0. 18. FDI showed an insignificant effect; however, trade openness was found to affect adversely. . .

Purpose: Rapid nutrient depletion in soils is one of the major problems that affect food production and food security in Sub-Saharan Africa. Studies have linked the growth of food crops with seasonal variation and differences in weather conditions. This study was conducted to assess the effects of various organic fertilizer formulations (OFFs) on the growth and yield of selected crops (Zea mays L.; Glycine max, TX 114 and Dioscorea rotundata Poir) during rainy and dry seasons to ensuring climate-smart agriculture. Methods: The OFFs used were plant-based (PB), animal-based (AB), rock-based (RB), organic mixture (OM-mixture of PB, AB and RB), synthetic/chemical (SC) while ordinary compost without fortification served as control. Effects of OFFs on growth parameters (number of leaves, plant height, stem girth, leaf area, and crop yield) of maize, yam and soybeans were assessed in plot experiments across the two seasons. Results: The RB gave highest growth performances in maize and soybean plots at both seasons when applied at 2. 5 t ha− 1. It also improved yam growth when applied at 2. 5 t ha− 1 (rainy season) and 3. 0 t ha− 1 (dry season) more than any other fertilizer. The largest yield of maize in the dry season was obtained from plots with PB at 2. 0 t ha− 1. The AB at 2. 0 t ha− 1 gave the largest soybean yield in the rainy season. Conclusions: Organic fertilizers enriched especially with rock-based and plant-based materials have the potential to ameliorate the threat of climate change and seasonal variation to food security.