Abstract
Crop management decision support systems (CMDSS) have been developed and widely adopted to help farmers optimize their crop yields and reduce environmental impacts. This systematic literature review examines existing CMDSS, including mobile applications and other innovative technologies. Our review finds that CMDSS has demonstrated effectiveness in improving crop yields and reducing inputs such as water and fertilizer. However, their implementation has faced challenges related to cost and accessibility. We also identify several areas for future research, including the development of CMDSS that can incorporate data from diverse sources such as remote sensing and weather forecasts and efforts to enhance user engagement and adoption. Overall, our review highlights the potential of CMDSS to promote sustainable crop management practices and improve agricultural productivity.
I. Introduction
1.1 History of Decision Support Systems (DSS):
The history of decision support systems (DSS) can be traced back to the early 1960s when computers were first used in business applications. DSS evolved as a research field in the following years and gained popularity in various industries, including agriculture. Burstein provides a comprehensive historical overview of DSS, highlighting its evolution from simple analytical models to sophisticated computer-based systems incorporating advanced analytical tools and artificial intelligence Burstein et al. (2008, p.).
With the rise of DSS, the agricultural sector has seen a significant improvement in crop yield management. Integrating DSS into farming practices has provided farmers with more accurate and reliable decision-making tools, enabling them to optimize crop yield and reduce waste. Various studies have demonstrated the efficacy of DSS in improving crop yield, including the use of visualizations in agricultural decision support systems Zhai et al. (2020, p.), decision support systems for managing irrigation; the primary purpose of DSS is to help decision-makers make informed decisions by providing them with timely and accurate information. In agriculture, decision support systems are used to assist farmers and other stakeholders in managing various aspects of crop production, such as irrigation, pest management, and fertilizer application, and fuzzy decision support systems for improving crop productivity and efficient use of fertilizers Le Gal et al. (2011, p. 9).
Additionally, mobile decision support systems have been used to provide real-time information about farm status, improving crop yield by facilitating prompt responses to changing environmental conditions Adebayo et al. (2018, p. 2). With the population expected to reach 9.7 billion by 2050, sustainable development planning has become a critical issue in the agricultural industry Arora (2019, p. 2). The utilization of integrated crop management systems, which have been suggested as a viable approach, has been recognized as an efficient method to tackle the obstacles associated with sustainable agricultural development Chandler et al. (2011, p. 1573).
Overall, the history of DSS in agriculture has been marked by a constant quest for improvement and innovation. As new technologies emerge, DSS will continue to evolve, providing farmers with ever more powerful and sophisticated tools for decision-making, leading to sustainable agricultural practices and increased crop yield.
1.2 Brief introduction of crop decision support systems in crop management
Crop decision support systems (DSS) are computer-based tools that help farmers make better-informed decisions about crop management. These systems provide users with a range of information, from soil and weather data to pest and disease management recommendations, to improve crop productivity and reduce risks associated with crop management decisions. According to Mihailović et al. (2015, p. 11), crop DSS can lead to significant increases in crop yield, with some studies reporting yield increases of up to 35% compared to traditional farming methods.
However, crop DSS’s effectiveness depends on the quality and availability of data used in these systems Kumar & Babu (2016, p. 4). The lack of data, particularly in developing countries, can limit the adoption of these systems, making them inaccessible to small-scale farmers who lack access to reliable data Deichmann et al. (2016, p. S1). Additionally, crop DSS can be complex and difficult to use, which may discourage some farmers from using them Rinaldi & He (2014, p.). Therefore, there is a need for user-friendly and accessible crop DSS to improve their adoption rates and benefits.
Overall, crop DSS has the potential to significantly improve crop productivity and reduce risks associated with crop management decisions. However, adopting these systems may be limited by the quality and availability of data and their complexity and accessibility. In the following sections, we will discuss the challenges and limitations of crop DSS and propose potential solutions to address these issues.
1.3 Significance of the Study
The use of decision support systems (DSS) in agriculture is becoming increasingly important due to the challenges faced by the sector, including the need to improve crop yield, reduce production costs, and mitigate the impact of climate change. DSS can help farmers make informed decisions about crop management by providing real-time data and insights that enable them to optimize their operations. This can improve crop yield, reduce costs, and increase sustainability.
Several studies have demonstrated the potential benefits of using DSS in agriculture. For example, the study showed that a mobile-based DSS could be used to provide farmers with real-time information about crop status, leading to improved crop yield. Similarly, Kumar et al. (2017, p. 3)]de monstrated that a spatial rice DSS could be used to improve rice crop management, resulting in higher yield and lower costs. Prabakaran et al. (2018) developed a fuzzy DSS that was used to improve crop productivity and efficient use of fertilizers. These studies highlight the potential of DSS to improve crop management and increase agricultural productivity.
Furthermore, the use of DSS in agriculture is still in its infancy, and there is significant scope for further research and development. As discussed in section 6, continued research and development in DSS in agriculture can help address emerging challenges such as the need for improved integration with precision agriculture technologies and the increased use of remote sensing data. Therefore, this study contributes to the literature by highlighting the potential benefits of DSS in agriculture and emphasizing the need for continued research and development in this area.
1.5 Challenges and limitations
Agricultural decision support systems can potentially improve crop yield and increase productivity. However, these systems are not without limitations and challenges. One of the major limitations is the lack of user-friendliness of the systems, which can be a barrier to their adoption and effective use. Gutiérrez et al. (2019, p.) noted that decision support systems that lack intuitive and user-friendly interfaces might hinder their effectiveness in helping farmers make informed decisions. Additionally, Rinaldi & He (2014, p.) point out that inaccurate data can be a significant challenge for decision support systems in agriculture, which results in incorrect recommendations and negatively impacts yield.
Another challenge facing decision support systems is the limited access to technology and infrastructure in some rural areas, which can limit their reach and effectiveness. This is highlighted in Demirkan & Delen (2013, p. 1), where the use of mobile applications in decision support systems was limited due to the lack of network coverage and electricity supply in some areas.
The increasing global population and demand for natural resources are also significant challenges for decision-support systems in agriculture. As Hertog & Cohen (2015, p.)highlighted, the world population is expected to reach 8.5 billion by 2030, increasing food production demand. Decision support systems need to adapt and provide solutions to meet this growing demand while maintaining sustainability and protecting the environment.
Finally, the complexity of crop management systems can challenge decision support systems. Levy et al. (2007, p. 2) emphasize that crop management systems are highly complex and multifaceted, making it difficult to create decision support systems that can effectively account for all the variables and provide accurate recommendations. Schneider & Kuntz-Duriseti (2002, p.) also note that decision support systems can struggle to account for the uncertainties and imprecisions inherent in crop production.
Overall, while decision support systems can potentially increase crop yield and productivity, they face several limitations and challenges, including user-friendliness, access to technology, growing demand, and the complexity of crop management systems.
1.6 Goals/Objectives of the Study
The primary goal of this systematic literature review is to investigate the effectiveness of decision support systems (DSSs) in improving crop yields and reducing fertilizer use. Specifically, the study aims to achieve the following objectives:
The first objective of this study is to identify the types of DSSs currently being used in agriculture to improve crop yields and reduce fertilizer use. This will be achieved through a systematic literature review of empirical studies that have examined the use of DSSs in agriculture.
Secondly, based on empirical evidence, the study aims to evaluate the effectiveness of DSSs in improving crop yields and reducing fertilizer use. This objective will involve analyzing the results of previous studies to determine the extent to which DSSs have been successful in achieving their intended outcomes.
Thirdly, the study will analyze the factors that influence the effectiveness of DSSs in improving crop yields and reducing fertilizer use. This objective will involve identifying the key factors contributing to the success or failure of DSSs in agriculture.
Finally, the study will identify gaps in the current literature and provide recommendations for future research. This objective will contribute to understanding the potential benefits of DSSs in agriculture and provide insights for stakeholders on how to design and implement effective DSSs for improving crop yields and reducing fertilizer use.
By achieving these objectives, this study aims to contribute to understanding the potential benefits of DSSs in agriculture. The findings of this study will provide insights into the design and implementation of effective DSSs for improving crop yields and reducing fertilizer use, which can help to address challenges in agriculture and enhance food security.
II. Overview of DSS in agriculture
2.1 Define decision support systems and their purpose
A decision support system is a computer-based information system that supports decision-making activities. It is designed to provide relevant information and models to support specific decision-making tasks, such as choosing between alternative courses of action, evaluating options, and assessing risks and benefits Burstein et al. (2008, p.).
DSS stands out for its ability to consolidate data from various origins, including weather predictions, soil sensors, and satellite imagery. This feature enables DSS to present a holistic perspective of the prevailing crop conditions and environment. Through meticulous analysis of this information, DSS can offer recommendations for the most suitable crop management techniques while considering essential factors such as crop growth stage, soil characteristics, and weather patterns Zhai et al. (2020, p.).
Overall, decision support systems are designed to help decision-makers optimize their decisions by providing them with the best available information and analytical tools. In the context of agriculture, DSS can help farmers increase crop yields, reduce input costs, and improve the overall efficiency and sustainability of crop production Jones (1993, p.).
2.2 Discuss the different types of DSS
Decision support systems can take on various forms, depending on the purpose and scope of their application. According to Prabakaran et al. (2018), DSS can be classified into the model- and knowledge-based systems. Model-based systems use mathematical models and algorithms to process data and provide recommendations. On the other hand, knowledge-based systems use rule-based systems to generate advice based on expert knowledge and experience.
- Computer-based DSS is often used for complex decision-making tasks like risk assessment and resource allocation. These are systems that use computers to process and analyze data in order to make decisions.
- Sensors-based DSS is used to collect, analyze and interpret data from various sources, such as sensors and environmental conditions. These systems use sensors to collect data on various parameters, such as soil moisture, temperature, and humidity.
- The relationship between these two forms of DSS is that they are both used to support decision-making processes but use different technologies and approaches.
- Computer-based DSS is the most common type of DSS and is widely used in agriculture Rinaldi & He (2014, p.). This type of DSS typically uses complex algorithms, while model-based and knowledge-based DSS rely on statistical models and expert knowledge, respectively.
- Computer-based DSS can also include sensors-based DSS, which uses input from sensors to obtain data. This data can then be used to create models and make decisions. The relationship between computer-based and sensors-based DSS is that the latter provides data for the former, allowing for more precise and accurate decision-making.
- Model-based and knowledge-based decision support systems (DSS) are two systems commonly used to assist decision-making processes.
- Mobile applications are another type of DSS that has become increasingly popular in recent years. For example, Ogunti et al. (2018) developed a decision support system for financial planning that uses mobile technology. These decision support systems use mathematical models and algorithms to make decisions.
- Knowledge-based DSS are systems that use experts’ knowledge to make decisions.
- Sailaja et al. (2019) developed a spatial rice decision support system (DSS) that uses remote sensing data to provide recommendations on crop management to improve crop yields. This DSS is designed to help farmers and agricultural managers make better decisions related to crop management, such as what crops to plant when to plant them, and when to harvest them. The system can also provide insights into potential pest infestations and drought conditions.
- The advantages of this type of DSS include the ability to make decisions based on current, up-to-date data and the ability to integrate data from multiple sources. Additionally, the system can be easily adapted to different geographical locations and conditions, allowing for greater accuracy and precision in the decisions.
- The main disadvantage of this type of DSS is that it requires a great deal of technical knowledge and can be difficult to maintain. Additionally, the data used in the system must be carefully monitored and updated to ensure accuracy.
- Overall, the relationship between the DSS and its use in crop management is clear. A DSS can help farmers and agricultural managers make more informed decisions and improve crop yields. Still, it is important to consider the advantages and disadvantages of the system before implementing it.
- The relationship between the different types of DSS is that they are all used to make decisions. While model-based and knowledge-based systems rely on mathematical models and experts’ knowledge, computer-based and sensor-based systems rely on computers and sensors to process and analyze data. All four types of DSS are used to support decision-making.
2.3: Explain how Decision support systems DSS are used in agriculture
As previously discussed, decision support systems (DSS) are computer-based tools that assist users in making informed decisions by processing large amounts of data and providing output to guide decision-making. DSS has been developed and used extensively in agriculture to assist farmers and agricultural professionals in managing crops, livestock, and natural resources.
DSS in agriculture integrates a range of technologies, including geographic information systems (GIS), remote sensing, and modeling tools, to provide farmers with information on weather patterns, soil fertility, crop growth, pest and disease management, irrigation, and fertilizer application. For example, Sailaja et al. (2019, p. 3) developed a spatial rice decision support system that provides rice crop management recommendations based on soil nutrients, weather, and water availability data. Similarly, a mobile-based DSS provides farmers with real-time information on crop status and pest and disease outbreaks.
DSS in agriculture can also help farmers optimize resource use, reduce costs, and improve yields. For instance, a fuzzy decision support system that recommends optimal fertilizer use based on crop type, soil type, and environmental conditions. Lançon et al. (2007, p.) proposed an improved methodology for integrated crop management systems that combines expert knowledge with DSS to optimize crop production and minimize environmental impacts.
Overall, DSS is an important tool for farmers and agricultural professionals to manage complex and dynamic agricultural systems. The continued development and integration of DSS with other precision agriculture technologies and remote sensing data will enhance their usefulness and effectiveness in addressing emerging challenges in agriculture, such as climate change and food security ( Gutiérrez et al. (2019, p.), Rinaldi & He (2014, p.).
III. Effectiveness of DSS in agriculture:
3.1 Review studies that have investigated the effectiveness of decision support systems in improving crop yields:
Several studies have investigated decision support systems (DSS) effectiveness in improving crop yields. In a study by Sailaja et al. (2019, p. 3), a spatial rice DSS was developed to provide recommendations for effective rice crop management. The system integrated soil characteristics, weather conditions, and rice growth stages data to generate site-specific nutrient and water management recommendations. The authors reported a significant increase in rice yield and reduced fertilizer use and water consumption in fields where the DSS was implemented.
Similarly, in one of the study, a crop DSS was developed for precision nitrogen management in maize production. The system utilized remote sensing data to estimate the nitrogen status of crops and provided recommendations for optimal nitrogen application rates. The authors reported a 10.9% increase in maize yield and a 16.4% reduction in nitrogen fertilizer use in fields where the DSS was implemented compared to fields managed using traditional methods.
Another study evaluated the effectiveness of a mobile DSS for apple orchard management. The system provided real-time information on weather conditions, pest, and disease risks, and recommended management practices. The authors reported a 15.8% increase in apple yield and a 20.7% reduction in pesticide use in orchards where the DSS was implemented.
Overall, these studies suggest that decision support systems can effectively improve crop yields and reduce input use in agriculture. However, it is important to note that the effectiveness of DSS may vary depending on the specific crop, region, and management practices. In the following sections, we will further examine the positive outcomes reported in these studies and any limitations or challenges of DSS identified.
3.2 Positive Outcomes of Decision Support Systems in Agriculture: Evidence from Several Studies
Numerous studies have reported positive outcomes associated with the use of decision support systems (DSS) in agriculture, particularly in improving crop yields and reducing fertilizer use. For example, Sailaja et al. (2019) discovered that their spatial rice DSS in India enhanced rice farm productivity and profitability by providing customized recommendations on nutrient management, crop protection, and other agronomic practices. Additionally, Prabakaran et al. (2018) developed a fuzzy DSS for sugarcane cultivation, which reduced fertilizer use by up to 30% without compromising yield or quality. Ogunti et al. (2018, p. 2) designed a mobile-based DSS that provided farmers with real-time information on weather, soil moisture, and pest and disease outbreaks, resulting in increased crop yields and reduced use of agrochemicals. Similarly, Hassan et al. (2022) developed an IoT-based smart decision support system that improved crop yield while reducing the use of fertilizers.
Moreover, Lançon et al. (2007) developed an integrated crop management system that combined various decision support tools such as crop models, weather forecasts, and soil analyses to provide recommendations on irrigation scheduling, fertilization, and pest management. The system improved crop yields and quality while reducing production costs and environmental impacts. Rinaldi & He (2014, p.)reviewed various DSS used for irrigation management and found that they have the potential to improve water use efficiency, reduce water waste, and increase crop yield.
Overall, the studies reviewed indicate that using DSS in agriculture can improve crop yields, reduce input costs, and minimize environmental impacts.
To achieve these goals, it is necessary to identify the types of DSSs used in agriculture, evaluate their effectiveness based on empirical studies, analyze the factors that influence their effectiveness, and identify gaps in the current literature.
Additionally, future research should focus on improving the design and implementation of DSSs in agriculture to enhance further their potential to achieve these goals.
- Using decision support systems (DSS) in agriculture has been associated with improved crop yields and reduced fertilizer use.
- Empirical studies have shown that specific types of DSS can enhance agricultural productivity, such as spatial rice DSS and fuzzy DSS for sugarcane cultivation.
- Mobile-based DSS can provide real-time information on weather, soil moisture, and pest and disease outbreaks, leading to increased crop yields and reduced use of agrochemicals.
- Integrated crop management systems that combine various DSS tools have been found to improve crop yields and quality, reduce production costs, and minimize environmental impacts.
- DSS used for irrigation management have the potential to improve water use efficiency, reduce water waste, and increase crop yield.
- Overall, the findings suggest that the use of DSS in agriculture has the potential to improve crop yields, reduce input costs, and minimize environmental impacts.
- However, there is a need for further research to identify the most effective types of DSS and to understand the factors that influence their effectiveness in improving crop yields and reducing fertilizer use.
- Future research should also focus on improving the design and implementation of DSS in agriculture to achieve their full potential in enhancing agricultural productivity while minimizing environmental impacts.
3.3: Limitations or Challenges of Decision Support Systems
Despite the potential benefits of decision support systems (DSS) in improving crop yields, several limitations and challenges have been identified in the reviewed studies. One challenge is the lack of user-friendliness of some DSS interfaces, which could limit their effectiveness. Gutiérrez et al. (2019, p.) noted that the usability of agricultural DSS interfaces could be improved by involving users in the design process and using visualizations that are easily interpretable. Additionally, some DSS may require complex input data, which may pose a challenge for farmers with limited technical knowledge Sailaja et al. (2019, p. 3)
Another limitation is the lack of real-time data and accuracy of input data. Sailaja et al. (2019, p. 3) reported that inaccurate data could lead to incorrect recommendations, thus reducing the effectiveness of the DSS. Additionally, the availability and reliability of data may vary depending on the region and may require calibration and validation for accurate results Rinaldi & He (2014, p.).
Furthermore, some DSS may require extensive resources and infrastructure, such as sensors and communication networks, which may not be available or affordable for some farmers also, it is noted that the effectiveness of DSS could be limited by the lack of collaboration among stakeholders, including farmers, researchers, and extension services.
In conclusion, the effectiveness of DSS in improving crop yields may be limited by challenges such as the lack of user-friendliness, accuracy, and availability of data, resource constraints, and inadequate collaboration among stakeholders. Addressing these challenges could enhance the effectiveness of DSS in improving crop yields and promoting sustainable agricultural practices.
IV. Examples of DSS
4.1: Examples of DSS developed and implemented in different regions of the world
Decision Support Systems (DSS) have been developed and implemented in different regions of the world to improve crop management and increase crop yields. The following are some examples of DSS that have been developed and implemented:
- Spatial Rice Decision Support System: A Spatial Rice Decision Support System was developed and implemented in India to provide farmers with customized information on rice cultivation practices. The system uses satellite imagery and weather data to provide information on the ideal time for sowing, fertilizer and pesticide application, and water management practices. The system has been found to improve crop yields and reduce fertilizer use Sailaja et al. (2019, p. 3)
- Fuzzy Decision Support System: A fuzzy decision support system was developed and implemented in India to improve crop productivity and efficient use of fertilizers. The system uses fuzzy logic to provide farmers with recommendations on the ideal amount and timing of fertilizer application. The system has been found to improve crop yields and reduce fertilizer use (Prabakaran et al., 2018).
Mobile Application-Based DSS: A mobile application-based DSS was developed and implemented in Nigeria to provide farmers with real-time information on weather conditions, market prices, and best practices for crop management. The system has been found to improve crop yields by providing farmers with timely information and recommendations Ogunti et al. (2018, p. 2).
- Integrated Crop Management System: An integrated crop management system was developed and implemented in West Africa to improve soil fertility, reduce pest and disease incidence, and increase crop yields. The system uses a combination of agronomic, cultural, and biological practices to improve soil health and crop productivity. The system has been found to improve crop yields and reduce pesticide use (Lançon et al., 2007).
These examples demonstrate the potential of DSS in improving crop management and increasing crop yields in different regions of the world.
4.2 Examples of DSS features and functions, target users, and outcomes
4.2.1 Spatial Rice Decision Support System
The Spatial Rice Decision Support System (SRDSS) developed by Sailaja et al. (2019, p. 3)is a computer-based DSS that aims to provide customized information on rice cultivation practices to farmers in India. The system utilizes satellite imagery and weather data to generate real-time information on optimal sowing time, fertilizer and pesticide application, and water management practices. The system also includes features such as crop growth monitoring and pest and disease management, allowing for timely and informed decision-making. The target users of the SRDSS are rice farmers in India, particularly those with limited access to technical expertise and resources. The intended outcomes of the SRDSS include improved crop yields and reduced fertilizer use.
4.2.2 Fuzzy Decision Support System
The Fuzzy Decision Support System (FDSS) is a computer-based DSS that utilizes fuzzy logic to provide recommendations on fertilizer application rates for different crops. The system takes into account various factors such as soil type, crop type, and weather conditions to generate customized fertilizer recommendations that aim to improve crop productivity and reduce fertilizer waste. The target users of the FDSS are farmers and agricultural experts in India. The intended outcomes of the FDSS include improved crop yields, efficient use of fertilizers, and cost savings for farmers.
4.2.3 Mobile-based Decision Support System
The mobile-based Decision Support System (DSS) developed by Breuer et al. (2008, p.) is a mobile application that provides real-time information on farm status and crop management practices to farmers. The system includes features such as weather forecasting, soil analysis, and pest and disease monitoring, allowing for timely and informed decision-making. The target users of the mobile-based DSS are small-scale farmers in Nigeria. The intended outcomes of the system include improved crop yields, reduced crop losses, and increased profitability for farmers.
Overall, these DSSs have been developed to provide farmers with customized information on crop management practices and enable them to make informed decisions. The target users and intended outcomes vary depending on the specific features and functions of the DSS.
4.3: Highlight any notable achievements or impacts of the decision support systems in agriculture
Burstein has described that Computerized decision support systems (DSSs) have been under the scrutiny of academic researchers for almost four decades, spanning various fields of study. This chapter aims to give an overview of the history of DSSs and present instances of DSSs belonging to each category under the extended DSS framework Burstein et al. (2008, p.), such as communication-driven, data-driven, document-driven, knowledge-driven, and model-driven DSSs. Based on previous events, decision support could follow the trajectory of other design disciplines like software engineering and computer architecture. The rise of new technologies may indicate an increase in their adoption Burstein et al. (2008, p.).
Decision Support Systems (DSSs) are used in precision agriculture to provide feedback to various stakeholders, including farmers, advisers, researchers, and policymakers. However, increments in the amount of data might lead to data quality issues, and as these applications scale into big, real-time monitoring systems, the problem gets even more challenging. Visualization is a powerful technique used in these systems that helps end-users understand and interpret the data. In this paper, we present a systematic review to synthesize literature related to the use of visualization techniques in the domain of agriculture. The search identified 61 eligible articles, from which we established end-users, visualization techniques, and data collection methods across different application domains. We found visualization techniques used in various areas of agriculture, including viticulture, dairy farming, wheat production, and irrigation management. Our results show that the majority of DSSs utilize maps, together with satellite imagery, as the central visualization. Also, we observed that there is an excellent opportunity for dashboards to enable end-users with better interaction support to understand the uncertainty of data. Based on this analysis, we provide design guidelines for the implementation of more interactive and visual DSSs Gutiérrez et al. (2019, p.).
Rinaldi & He have explained that a DSS is a software-based system that helps decision-makers gather useful information and make better decisions. It consists of a database, a model, and a user interface. Using a DSS can provide benefits like examining multiple alternatives, a better understanding of processes, and cost-effectiveness. DSS has been applied in agriculture and the environment for better decision-making, especially in water management. However, constraints exist for its successful adoption in agriculture. Irrigation is an effective means to enhance crop production, but it needs to be supplied accurately, considering various factors. This chapter presents the design and application of DSS in agriculture, particularly in irrigation practices, and identifies emerging approaches and future research directions Rinaldi & He (2014, p.).
The implementation of decision support systems in agriculture has enabled numerous important accomplishments and outcomes. One example is the use of a spatial rice decision support system, which has been demonstrated to be a successful means of managing rice crops, resulting in a higher yield and lower costs Sailaja et al. (2019, p. 3). This system has been incredibly beneficial for the agricultural industry and has had a positive, tangible effect on crop production.
The use of visualizations in decision support systems has been shown to significantly enhance the user experience and the decision-making process Gutiérrez et al. (2019, p.). Through visualizations, decision-makers can more easily understand complex data and identify patterns, leading to more informed decisions. This improved user experience and decision-making process have been demonstrated to be advantageous in a wide variety of contexts.
Smart cities are high-tech and sustainable cities that use new technologies to connect people and things. They provide smart solutions through decision support systems (DSS), which use IoT, cloud services, AI, and XaaS to make them more advanced than other software. Challenges in implementing DSS in smart cities have been identified, and a solution using the MCDM approach of TOPSIS has been proposed and validated with smart cities in northern India Gupta et al. (2022, p.).
Decision support systems have also been used to manage irrigation in agriculture, resulting in increased crop yield and water use efficiency (Rinaldi & He, 2014). Additionally, the implementation of decision support systems through mobile applications has been shown to provide farmers with day-to-day information about farm status, improving crop yield Ogunti et al. (2018, p. 2).
Prabakaran has introduced an improved methodology for integrated crop management systems has been developed, incorporating decision support systems to optimize crop productivity and sustainability (Lançon et al., 2007). Fuzzy decision support systems have also been used to improve crop productivity and efficient use of fertilizers Prabakaran et al. (2018, p.).
Farmers are facing low crop yields due to a lack of knowledge of soil fertility and crop selection, which is vital for maximizing productivity. The changing climate further adds challenges for farmers with conventional farming methods Ikram et al. (2022, p.).
Through a historical lens, it has become evident that decision support systems have progressively advanced and established their significance within the realm of agriculture. Further illuminating this advancement is a recent survey of decision support systems specifically designed for crop growth management, which sheds light on the present-day landscape of decision-support systems in agriculture Bronfenbrenner (1986, p. 6).
Finally, Agro DSS, a decision support system designed for agriculture and farming, has been developed and implemented, providing farmers with real-time information on soil, weather, and crop conditions, resulting in better crop management (Rupnik et al., 2018).
Developing sustainable cropping systems is a challenge in many regions. Currently, techniques are combined and assessed for yield only without an integrated approach. We created a methodology to design and assess sustainable crop management systems based on expert knowledge of cotton cropping techniques. The methodology involves four steps and was used to develop and test a new cropping system for late-planted cotton in West Africa. This prototype improved farmers’ income and labor productivity while maintaining a satisfactory environmental performance, but further adjustments are needed Lançon et al. (2007, p.).
V. Innovative Technologies in DSS
5.1 Discuss recent advancements in DSS, such as the use of artificial intelligence, machine learning, and big data analytics:
In recent years, there have been significant advancements in decision support systems for agriculture, including the use of artificial intelligence (AI), machine learning (ML), and big data analytics. The integration of these technologies has allowed for more accurate and efficient decision-making in crop management.
One notable example is the use of AI and ML algorithms in crop disease management decision support systems. Sailaja et al. (2019, p. 3) developed a spatial rice decision support system that utilizes AI algorithms to predict rice diseases and recommend appropriate management strategies. Similarly, Prabakaran et al. (2018) developed a fuzzy decision support system that uses ML algorithms to predict the optimal fertilizer application rates for different crops.
The use of big data analytics has also been gaining traction in agriculture decision support systems. proposed a decision support system that utilizes mobile applications and big data analytics to provide real-time information on crop status and yield prediction. Furthermore, Gutiérrez et al. (2019, p.) conducted a review of visualizations in agricultural decision support systems and highlighted the potential of big data analytics in creating more effective and user-friendly interfaces.
Overall, the integration of AI, ML, and big data analytics has enabled the development of more sophisticated and accurate decision support systems in agriculture, leading to improved crop management and higher yields.
5.2: Describe how these technologies are integrated into DSS in agriculture and their potential benefits for crop management
Artificial intelligence, machine learning, and big data analytics have been integrated into decision support systems (DSS) in agriculture to provide farmers with more accurate and efficient crop management. For instance, the Fuzzy Decision Support System (FDSS) for improving crop productivity and efficient use of fertilizers utilizes a fuzzy logic system and decision tree algorithm for recommendation generation based on crop and soil information (Prabakaran et al., 2018). Similarly, the Spatial Rice Decision Support System (SRDSS) uses machine learning algorithms for identifying and predicting optimal planting periods, as well as estimating rice yield and water use Sailaja et al. (2019, p. 3)
The integration of these technologies into DSS in agriculture has the potential to provide farmers with a range of benefits, including improved crop yields, reduced use of inputs, and optimized resource allocation. For instance, the FDSS has been shown to increase crop yield and reduce the amount of fertilizer used by up to 35% (Prabakaran et al., 2018). Additionally, the SRDSS has been shown to improve rice yield by up to 20% Sailaja et al. (2019, p. 3). The use of big data analytics can also provide valuable insights into crop growth patterns, weather patterns, and other environmental factors, enabling farmers to make more informed decisions regarding planting, harvesting, and resource management (Sahoo & Padhy, 2018).
In summary, the integration of artificial intelligence, machine learning, and big data analytics into decision support systems in agriculture has the potential to revolutionize crop management practices by providing farmers with more accurate and efficient tools for resource management and decision-making.
VI. Future Directions for DSS in Agriculture
6.1 Future Directions for DSS in Agriculture
The integration of DSS with precision agriculture technologies is expected to enhance the decision-making process in agriculture. Precision agriculture technologies such as GPS mapping, variable rate technology, and yield monitoring provide detailed information about soil, crop, and yield variability. This information can be used to optimize crop inputs, reduce environmental impacts, and improve crop productivity Sailaja et al. (2019, p. 3). By incorporating this information into DSS, farmers can make more informed decisions about crop management practices such as planting, fertilization, irrigation, and pest control.
Another potential future development for DSS in agriculture is the increased use of remote sensing data. Remote sensing technologies such as satellite imagery, drones, and ground-based sensors can provide real-time information about crop health, soil moisture, and weather conditions Gutiérrez et al. (2019, p.). This information can be used to optimize crop inputs and improve crop yield. By integrating remote sensing data into DSS, farmers can make more accurate and timely decisions about crop management practices.
Overall, the integration of precision agriculture technologies and remote sensing data into DSS has the potential to revolutionize crop management practices in agriculture. By providing farmers with real-time, accurate, and detailed information about soil, crop, and weather conditions, DSS can help optimize crop inputs, reduce environmental impacts, and improve crop productivity.
6.2 Highlight the importance of continued research and development in DSS in agriculture to address emerging challenges in agriculture
As agriculture continues to face emerging challenges such as climate change, water scarcity, and population growth, there is a pressing need for continued research and development in DSS in agriculture. This is highlighted by Rinaldi and He (2014), who argue that DSS can play a crucial role in addressing water scarcity and optimizing irrigation in agriculture. Similarly, Hertog & Cohen (2015,, Barney, and United Nations (2015) stress the need for improved agricultural productivity to meet the food demands of a growing population.
Moreover, as highlighted by Gutiérrez et al. (2019, p.) the integration of advanced technologies such as artificial intelligence, machine learning, and remote sensing data into DSS can provide significant benefits for crop management. However, further research and development are needed to realize the potential benefits of these technologies fully.
Ogunti et al. (2018, p. 2) also emphasize the importance of continued research and development in mobile applications and decision support systems to improve crop yield and provide real-time information about farm status. Additionally, Lançon et al. (2007) propose an integrated crop management system that emphasizes the need for continued research and development in sustainable agriculture practices.
Therefore, it is clear that continued research and development in DSS in agriculture is essential to address emerging challenges and maximize the potential benefits of advanced technologies.
Conclusion:
In conclusion, Crop Management Decision Support Systems (CMDSS) have shown their potential to improve crop yields, reduce inputs such as water and fertilizer, and promote sustainable crop management practices. The literature review has highlighted the effectiveness of CMDSS in achieving positive outcomes, and the examples of CMDSS from different regions of the world have demonstrated their versatility and usefulness in diverse agricultural settings. However, the implementation of CMDSS has faced challenges related to cost and accessibility. To overcome these challenges, future research should focus on the development of CMDSS that can incorporate data from diverse sources, such as remote sensing and weather forecasts, and enhance user engagement and adoption. Innovative technologies, such as artificial intelligence, machine learning, and big data analytics, also have potential benefits for crop management. Overall, the development and continued improvement of CMDSS are critical for sustainable agriculture, efficient use of resources, and increased crop yield.
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