Improved Water Management Research Articles

Ramped step flow field to enhance mass transfer capacity and performance for PEMFC

Proton exchange membrane fuel cells (PEMFC) hold great promise for various energy-conversion technologies. Flow channel structure inside bipolar plate plays an important role in water transport, gas distribution and heat transfer for PEMFC. Herein, a novel design of ramped step structure has been proposed, in which the ramps are introduced into the front side of step structures to form a continuous and smooth flow field. The cell performance and improvement mechanism have been investigated by COMSOL. The results show that the net power density of PEMFC with ramped step flow field (RSFF) exhibits an increase of up to 17.55 %, 2.82 %, and 2.29 % when compared to conventional parallel flow field (CPFF), ramp flow field (RFF), and step flow field (SFF), respectively. RSFF can achieve higher and more uniform distribution of oxygen cross the entire gas diffusion layer and catalytic layer. Additionally, increasing the slope angle in RSFF will help to enhance the capacity of mass transfer within a certain range. Furthermore, RSFF improves water management by significantly increasing gas flow velocity at the end of the channel. This work paves the way for the design of tapering flow channels in PEMFC, opening up new avenues for research in this field.

Revolutionizing Agriculture: A Case Study of IBM's AI Innovations

Background / Purpose: The development of computer systems that can carry out tasks that traditionally require human intelligence is referred to as artificial intelligence (AI). It entails the development of intelligent machines that can reason, learn, solve issues, and make judgements. A fast-developing topic, AI has enormous ramifications for many different businesses and facets of society. By leveraging advanced algorithms and data analysis techniques, AI systems can process and interpret large amounts of information in real-time, enabling them to extract valuable insights and patterns that may be difficult for humans to perceive.AI technologies have a wide range of applications across multiple domains, including healthcare, finance, transportation, manufacturing, education, entertainment, including agriculture. When referring to AI in the context of agriculture, we mean the use of advanced analytics and computational algorithms to analyse massive volumes of agricultural data, anticipate the future, and give farmers and stakeholders useful information. The main goal of using AI to agriculture is to increase efficiency, sustainability, and productivity across a range of farming operations, also to create smart and efficient systems that can monitor, analyze, and control water resources in real-time, leading to improved water management and sustainable agricultural practices, thereby addressing the challenges faced by the agricultural sector.AI offers significant potential to optimize water usage, enhance crop productivity, and mitigate environmental impact. In this paper, IBM, a significant provider of services in the sector of agriculture in recent years, is examined. Objective: In this case study, artificial intelligence is the main topic with particular emphasis on IBM's agricultural technology. Design/Methodology/Approach: Academic works published in a variety of peer-reviewed journals, conferences, and business websites provided the necessary information and specifics for this case study on IBM. Findings/Result: This study is primarily concerned with the usefulness and significance of AI in the modern world. The demand for and necessity of the numerous resources. provided by IBM, discussion topics include the company's business plan, varied results, top clientele, and numerous service types. Originality/Value: The analysis gives a concise description of IBM, the types of data collected and managed, information on artificial intelligence (AI), and the numerous AI services offered by IBM. Paper Type: Case study on the importance of storage and computing requirements for AI services offered by different service. providers, with a focus on IBM.

Sensor platform for assessment of water usage patterns in informal settlements

Rapid urbanization has intensified pressures on global water systems, particularly impacting informal settlements. Understanding water usage patterns within these settlements is of importance for better addressing water scarcity issues. Current methods for gaining information about water within these settings tend to lack spatio-temporal granularity and miss complex patterns of behavior related to water usage. As a consequence, there is a shortage of the reliable quantitative measurements needed to improve water management processes and modeling. Here we introduce a low-cost sensing platform for water assessment in informal settlements. Households within these types of settlements, lacking water utility connections and piping, often use storage tanks and buckets to distribute, store, and consume water; hence, the platform consists of four distinct sensor modules that can be placed on these types of water infrastructure. Evaluated in controlled settings, the sensors prove to be reliable for measuring water quantity, quality, and usage. Field testing within an informal community in Mexico reveals that the system can comprehensively track multiple tank storage levels, assess water quality, and capture bucket usage patterns without disrupting a household’s common activities or infrastructure. Our validation shows the technique’s potential to improve water management in informal communities, while opening opportunities for enhancement of water-related research and policy making through combinations of top-down and bottom-up interventions.

Technical Overview of the TexMesonet—A Network of Networks for Improved Water Management and Prediction in Texas

Abstract Accurate, timely, and accessible meteorological and soil moisture measurements are essential for a number of applications including weather forecasting, agricultural decision-making, and flood and drought prediction. Such data are becoming increasingly available globally, but the large number of networks and various data reporting formats often make utilization of such data difficult. The TexMesonet is a “network of networks” developed within the state of Texas to collect, process, and make public data collected from more than 1700 monitoring stations throughout the state. This paper describes the TexMesonet, with special attention paid to monitoring sites installed and managed by the Texas Water Development Board. It also provides a case study exemplifying how these data may be used and gives recommendations for future data applications.

Enhancement of two-phase flow and mass transport by a two-dimensional flow channel with variable cross-sections in proton exchange membrane fuel cells

A novel two-dimensional flow channel (TDFCH) with variable cross-sections geometrically controlled by sinusoidal function is proposed for proton exchange membrane fuel cells (PEMFCs). Two-phase flow simulations and visualisation experiments are combined to reveal the mechanisms for improved water management and enhanced reactant transport. Results show that the centrifugal force generated by the vortex pushes the liquid water away from the central region of the flow channel, forming droplet and film flows to avoid blockage of the flow channel. The reactant gases accumulate on the windward side of the variable-diameter structures, leading to an increase of the local pressure and facilitating fuel diffusion into the porous electrode. The fluid inertia enhances mass transfer of reactants and removal of liquid water inside the porous electrode. The amplitude A of the flow channel based on sinusoidal curve plays a dominant role in improving the performance of the TDFCH. Under the same testing conditions, when amplitude A = 0.2 and period T = 2, the peak power density of the TDFCH-based cell reaches 517.9 mW cm−2, which is 26 % higher than the conventional straight flow channel. The newly designed TDFCH provides an efficient solution to realize performance improvement and long-term stable operation of PEMFCs.

Saxitoxin hazard! Detection of toxic algae in molluscs breeding areas in North-Western Adriatic Sea

Abstract Background Saxitoxin (SXT) can cause the paralytic shellfish poisoning (PSP) with severe impacts on both human health and marine ecosystems. Dinoflagellates are the major marine producers of STX and other variety of secondary toxic metabolites that can gather in molluscs becoming a risk for consumers’ poisoning. The aim of this work was to investigate the presence of the main algal species producing SXT in an area intended for molluscs breeding. Methods During 2020-2023, 40 seawater samples were collected from two macro-areas in the Adriatic Sea (North-Western) at different distances from the coast. After water filtration, DNA was extracted (DNeasy PLANT Mini Kit) and analysed with Real Time PCR for Alexandrium minutum and Gymnodinium catenatum detection. The possible ability to produce SXT was confirmed by sxt4A gene detection. Molecular results were compared with the microscopy gold-standard method. Results A. minutum was detected in 62.5% (CI 95% 0.47-0.75) of the samples, with 84% (CI 95% 0.65-0.94) of them possibly producing SXT. G. catenatum was found in 95% (CI 95% 0.83-0.99) of samples, with 73.7% (CI 95% 0.58-0.85) of them presenting STX gene. By microscopy, only Alexandrium spp. were identified without successful species identification. No other saxitoxin-producing genera or species were observed. Conclusions Our results underlined that A. minutum and G. catenatum can play an important role in the production of SXT, confirming their harmful presence in the areas intended for molluscs breeding. Microscopic identification can lead to an underestimation of the presence of potentially toxic algal species, stressing the relevance of improving molecular tools also in this field. These findings highlighted the importance of reporting and monitoring toxic algae presence, given the saxitoxin impact for food safety, and its significance for public health. Key messages • Saxitoxin-producing algae detection through molecular methods in molluscs breeding areas may represent an early warning useful to improve food safety and human health. • Harmful algal monitoring can improve water management in a One Health perspective.

Magnetically treated water affects Melissa officinalis L growth, nutritional status and essential oil compounds

Drip irrigation is one of the agricultural techniques mostly used to grow a wide variety of vegetable crops. Studies focused on improving this technique based on using magnetized water, as well as on investigating its effects on medicinal plant production processes, remain incipient in the literature. Some benefits of this technique are the optimization of the quality and quantity of irrigation water, economy of the water absorbed by the plants, improvement in the vegetative growth and productivity, reduction of the use of fertilizers and the greater availability of nutrients in the soil for crops compared to irrigation using conventional water. Thus, the aim of the current study is to assess the effect of different magnetized and non-magnetized water irrigation levels on Melissa officinalis growth, nutritional status and essential oil compounds in plants grown in protected environment. The experimental design consisted of completely randomized design with two water types (magnetized and non-magnetized) and four irrigation levels (25%, 75%, 100% and 125% of crop evapotranspiration - ETc) with four replications. Irrigation conducted with different magnetized water levels had significant effect on the accumulation of nitrogen, phosphorus, sulfur, and of all micronutrients in plants’ leaves, and on soluble protein contents. The study also demonstrated significant effects of magnetically treated irrigation water on major chemical compounds such as citral (neral + geranial) and citronellal on the leaves of lemon balm plants. Different water levels significantly affected plant dry weight production parameters. In this study it was shown that the plant treated with magnetized water required less amount of water applied being a promising potential in saving water resources that will be of significant importance in the future. Overall, the results need to be further tested under field conditions to assess the usefulness of magnetic irrigation water treatment.in cultivation with medicinal, aromatic and spice plants. It is hoped that this study can contribute to improving water management, reducing the environmental impact without implying great costs for the producer.

Smart Crop Cultivation System Using Automated Agriculture Monitoring Environment in the Context of Bangladesh Agriculture.

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh.

Semiautomatic Mapping of Center Pivot Irrigated Areas Using Sentinel-2 Images and GEOBIA Approach

Image analysis and feature extraction of remoted sensing data are significant for mapping irrigated agriculture areas as a source of information to improve water management and agricultural planning. This paper presents an image segmented base approach GEOBIA (Geographic Object-Based Image Analysis) to extract irrigated areas by Center Pivot Irrigation System (CPIS). This study suggests a semi-automated recognition of circular patterns for the mapping of irrigated regions by center pivots, using Sentinel -2 MSI images, 10 meters spatial resolution. A set of images from different seasons, humid and dry are used to maximize de CPIS’s occurrence. A multiresolution segmentation method was applied, and a large number of segment-based shape features was extracted and used as input to a feature selection procedure (shape descriptors: Area; Compactness; Circularity Factor; Length/Width; Radius of smallest enclosing ellipse; and Roundness). In addition, another shape descriptor “Circularity Factor” was developed in this research and played an important role during preliminaries classification processes. The accuracy assessment of preliminaries classifications has validated used the Circularity Factor together with the other chosen shape descriptors to reach better results to CPIS’s detection. Furthermore, 86.23% of the CPIS mapped in the classification process is in accordance with the ground truth map. This methodology can be used to map large areas in a relatively short time and provides a tool for monitoring irrigated areas.

MANAGING THE PRODUCTIVITY PROCESS IN AGRICULTURE, A FRAMEWORK FOR IMPROVING THE MARKET POSITION OF AGRICULTURE OF THE REPUBLIC OF SRPSKA

Agriculture in Republika Srpska is the second largest sector with products of low added value. The biggest challenge is strengthening resilience and increasing competitiveness in the market. This paper analyzes the possibilities for improving the position of small farms and establishing a framework for more efficient development of entrepreneurship in the agricultural sector of the Republic of Srpska. The methods used in agro-economic research are used in the work, namely description, induction, deduction, synthesis, compilation, and data comparison and analysis. The increase in productivity is directly dependent on the diversification of production, the application of positive practices and increased resistance to climate change. Improved safety standards of agricultural products and food quality directly affect increased competitiveness and the potential for access to higher value markets. The application of climate-smart irrigation technologies and the improvement of water management in agriculture represents a revolutionary turning point in agriculture.

Machine learning-based detection of irrigation in Vojvodina (Serbia) using Sentinel-2 data

ABSTRACT With rapid population growth and the high influence of climate change on agricultural productivity, providing enough food is the main challenge in the 21st century. Irrigation, as a hydrological artificial process, has an indispensable role in achieving that goal. However, high pressure and demand on water resources could lead to serious problems in water consumption. Knowing information about the spatial distribution of irrigation parcels is essential to many aspects of Earth system science and global change research. To extract this knowledge for the main agricultural region in Serbia located in the moderate continental area, we utilized optical satellite Sentinel-2 data and collected ground truth data needed to train the machine learning model. Both satellite imagery and ground truth data were collected for the three most irrigated crops, maize, soybean, and sugar beet during 3 years (2020–2022) characterized by different weather conditions. This data was then used for training the Random Forest-based models, separately for each crop type, differentiating irrigated and rainfed crops on the parcel level. Finally, the models were run for the whole territory of Vojvodina generating 10 m resolution maps of irrigated three crops of interest. With overall accuracy for crops per year (2020: 0.76; 2021: 0.78; 2022: 0.84) results showed that this method could be successfully used for detecting the irrigation of three crops of interest. This was confirmed by validation with the national dataset from Public Water Management Company “Vode Vojvodine” which revealed that classification maps had an accuracy of 76%. These maps further allow us to understand the spatial dynamics of the most important irrigated crops and can serve for the improvement of sustainable agricultural water management.

Confession and greenwashing: the impact of water information disclosure on firm value

Water pollution has become a serious problem in recent years, and firms are one of the main culprits for this environmental issue. Encouraging companies to improve water management behavior is necessary to protect water resources. Based on panel data of Chinese A-share listed companies in water-sensitive industries, this study used text analysis techniques to study the impact of corporate water information disclosure on pollutant emissions, strategy on firm value, and the U-shaped moderating role of local governments’ environmental regulation. The results show that (1) an increase in the amount of emission information texts to confess the water status can help enhance firm value. (2) The increase in the amount of strategic information texts, both the tone of emission, and strategic information texts have negative impacts on firm value, which help enterprises greenwash. Further analyses find a U-shaped moderating effect of governments’ environmental regulation on the relationships between emission information disclosure, strategic information disclosure and firm value, and varies under the different ISO14001 certification situations and life cycles. This research explains the effectiveness of corporate water information disclosure and the influence of governments’ environmental regulation, which plays a vital role in promoting firms to improve their environmental behaviors, reduce water pollution, and further achieve green development.

The potential development way of Azerbaijan's melioriation and water management area

The article is devoted to improvement of the agriculture of the Republic of Azerbaijan, creation of an abundance of crops, solution of the reliable supply of food products to the population, provision of efficient use of water resources in the country, improvement of water management, etc. The ways of development of the country's reclamation and water management by summarizing the results of multi-year data and scientific-research works were examined and noted, the reforms carried out in this field, major construction works, the potential development of grain growing, cotton growing, tobacco growing, cocoon growing and other fields in the country, as well as new information on the melioration measures performed as a result of the implementation of the tasks related to the creation of agroparks and large farms was examined, as well as information was given on the works being carried out on the improvement of the melioration potential, and the improvement of the water supply and melioration condition of the irrigated lands with the implementation of the measures planned for the future, the improvement of the newly irrigated lands as a result of its commissioning, reliable provision of bread and food products to the country's population at the expense of domestic production; Information about the implementation of various projects for the protection of buildings and other infrastructure objects from the harmful effects of floods and floodwaters, and for their more efficient use due to the regulation of water resources of rivers has been mentioned.

Effect of Hydrophilic and Hydrophobic Composite Microporous Layer Coated Gas Diffusion Layers on PEFC Performance

Addressing the voltage loss problem at high current densities is crucial to expand PEFCs output power. One approach to improve water management and extend the cell operating range is modifying the microporous layer (MPL) coated on the gas diffusion layer (GDL). This study developed a novel MPL coated GDL preparation method featuring randomly allocated hydrophobic and hydrophilic pores within the same layer, which was proven by the water permeability test. This design allows excess water at the interface between the catalyst layer (CL) and MPL to be expelled through the hydrophilic pores while maintaining gas transport via the hydrophobic pores to reduce the mass transport resistance. Various hydrophobic and hydrophilic materials were tested, and the different compositions were evaluated to determine the appropriate combination. The results demonstrated that the composite MPL further reduced total oxygen transport resistance compared to the conventional hydrophobic MPL.

Predicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillo

This study focuses on assessing the accuracy of supervised machine learning regression algorithms (MLAs) in predicting actual crop evapotranspiration (ETc act) for a deficit irrigated vineyard of Vitis vinifera cv. Tempranillo, influenced by a typical Mediterranean climate. The standard approach of using the Food and Agriculture Organization (FAO) crop evapotranspiration under standard conditions (FAO-56 Kc-ET0) to estimate ETc act for irrigation purposes faces limitations in row-based, sparse, and drip irrigated crops with large, exposed soil areas, due to data requirements and potential shortcomings. One significant challenge is the accurate estimation of the basal crop coefficient (Kcb), which can be influenced by incorrect estimations of the effective transpiring leaf area and surface resistance. The research results demonstrate that the tested MLAs can accurately estimate ETc act for the vineyard with minimal errors. The Root-Mean-Square Error (RMSE) values were found to be in the range of 0.019 to 0.030 mm·h⁻¹. Additionally, the obtained MLAs reduced data requirements, which suggests their feasibility to be used to optimize sustainable irrigation management in vineyards and other row crops. The positive outcomes of the study highlight the potential advantages of employing MLAs for precise and efficient estimation of crop evapotranspiration, leading to improved water management practices in vineyards. This could promote the adoption of more sustainable and resource-efficient irrigation strategies, particularly in regions with Mediterranean climates.

Linking multivariate statistical methods and water quality indices to evaluate the natural and anthropogenic geochemical processes controlling the water quality of a tropical watershed.

The improvement of water management requires monitoring techniques that accurately evaluate water quality status and detect the effects of land use changes on water chemistry. This study aimed to evaluate how multivariate statistical methods and water quality indices can be applied together to evaluate the processes controlling water chemical composition and the overall water quality status of a tropical watershed. Thirty-four water samples were collected in the Formoso River basin, located on the border of the Amazon Forest. Water parameters were measured in situ using a multiparameter and in the lab using spectroscopic and volumetric techniques. The water quality dataset was interpreted through principal component analysis, multivariate linear regression, and water quality indices. Statistical methods allowed us to identify the sources and geochemical processes controlling water quality chemistry, which were carbonate dissolution, runoff/erosion, nutrient input due to anthropogenic activities, and redox reactions in flooded zones. They were also used to create linear functions to evaluate the effects of land use changes on the geochemical processes controlling water chemistry. Conversely, the water quality indices provide information about the overall condition of the water. The Weight-Arithmetic Quality Index correctly evaluates water suitability for its multiple uses, according to the Brazilian guidelines. Conversely, the Ontario Water Quality Index is not suitable to evaluate the water quality of tropical rivers, since the usual higher water temperature and the low oxygen contents associated with tropical environments result in biased water quality evaluations by this index.

A deep neural network with electronic nose for water stress prediction in Khasi Mandarin Orange plants

Water stress is a significant environmental factor that hampers plant productivity and leads to various physiological and biological changes in plants. These include modifications in stomatal conductance and distribution, alteration of leaf water potential & turgor loss, altered chlorophyll content, and reduced cell expansion and growth. Additionally, water stress induces changes in the emission of volatile organic compounds across different parts of the plants. This study presents the development of an electronic nose (E-nose) system integrated with a deep neural network (DNN) to detect the presence and levels of water stress induced in Khasi Mandarin Orange plants. The proposed approach offers an alternative to conventional analytical methods that demand expensive and complex laboratory facilities. The investigation employs the leaf relative water content (RWC) estimation, a conventional technique, to evaluate water stress induction in the leaves of 20 plants collected over a span of 9 days after stopping irrigation. Supervised pattern recognition algorithms are trained using the results of RWC measurement, categorising leaves into non-stressed or one of four stress levels based on their water content. The dataset used for training and optimising the DNN model consists of 27 940 samples. The performance of the DNN model is compared to traditional machine learning methods, including linear and radial basis function support vector machines, k-nearest neighbours, decision tree, and random forest. From the results, it is seen that the optimised DNN model achieves the highest accuracy of 97.59% in comparison to other methods. Furthermore, the model is validated on an unseen dataset, exhibiting an accuracy of 97.32%. The proposed model holds the potential to enhance agricultural practices by enabling the detection and classification of water stress in crops, thereby aiding in water management improvements and increased productivity.

Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI

Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day−1, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images.

Blind shots: non‐natural mortality counteracts conservation efforts of a threatened waterbird

AbstractWaterbirds are particularly affected by the high hunting pressure they face in many regions, which in some cases is compromising conservation actions for threatened species. The marbled teal Marmaronetta angustirostris is one of the most endangered waterbirds in Europe. In order to restore its population, several conservation actions have recently been undertaken, including a population reinforcement programme in Spain using captive‐bred birds. With the aim of assessing the success of the reinforcement programme to establish a long‐term self‐sustaining population, we identified mortality causes of marbled teal, evaluated the survival of individual birds of the reinforcement programme and estimated the viability of the population under different management scenarios. We used data from wild and captive‐bred individuals tracked by GPS since 2018 (n = 42) and from a mark–recapture programme initiated in 2015 (n = 297). We recovered 15 dead birds or transmitters: 20% died of natural causes, 60% of non‐natural causes (including all anthropic causes) and 20% of unknown causes. Furthermore, the GPS tags of 24 birds unexpectedly stopped transmitting without any indication of malfunction, and for 66.7% of these disappeared birds, the cessation was suspected to be caused by illegal shooting. Survival during the hunting season was higher for males (31.3%) than for females (12.5%), and for the wild (50%) than for the captive‐bred birds (9.4%), probably due to differences in migration patterns to North Africa. Population viability models revealed that maintaining the breeding population at the current mortality rates is only possible with a permanent release programme of captive‐bred individuals, and that in order to establish a self‐sustaining population, non‐natural mortality would have to be reduced by at least 40%. We recommend management measures to reduce marbled teal mortality, such as limiting legal hunting to hours with clear visibility, prosecuting illegal shootings, controlling exotic predators and improving water management to reduce disease outbreaks. Some improvements can be implemented in captive‐breeding programmes, such as earlier release times and incorporating anti‐predator training.

English

Conservation agriculture (CA) aims to achieve productive and sustainable farming while preserving the natural resource base and environment. This article explores how CA aligns with Sustainable Development Goals (SDGs) of the United Nations and contributes to their achievement, particularly SDG 2: Zero Hunger, SDG 6: Clean Water and Sanitation, SDG 12: Responsible Consumption and Production, SDG 13: Climate Action, and SDG 15: Life on Land. CA has gained significant attention as a sustainable farming system on over 200 million hectares of global cropland, with an annual increase of 10 million hectares since 2008. The principles of minimum soil disturbance, diverse crop rotations and permanent soil cover help improve soil health, nutrients and water management, and contribute to biodiversity preservation. By encouraging sustainable agriculture and enhancing nutrition, SDG 2 seeks to end hunger. CA practices enhance soil health and nutrient availability, increasing crop productivity and reducing reliance on synthetic inputs like pesticides and fertilizers. The resilience of CA to climate change challenges further supports food production. For SDG 6, minimum tillage and soil cover components of CA help reduce soil erosion, resulting in cleaner water by preventing sedimentation in water bodies. Improved water management and conservation contribute to sustainable water use and sanitation. SDG 12 seeks responsible consumption and production patterns. CA promotes efficient resource use, reduces waste, and minimizes the negative environmental impact of agricultural inputs, leading to sustainable production systems with less reliance on external inputs. Regarding SDG 13, CA practices improve soil and water management, increase soil carbon absorption, and lower greenhouse gas emissions, all of which help agricultural systems become more resilient to climate change. Lastly, SDG 15 focuses on life on land preservation. CA's promotion of biodiversity through cover cropping and decreased chemical use provides habitats for organisms, supporting ecosystem services and sustainable land use. In conclusion, CA plays a significant role in achieving multiple SDGs by promoting sustainable agriculture, conserving resources, mitigating climate change, and preserving biodiversity. The adoption and expansion of CA practices are essential for a sustainable and resilient future