Sustainable Development Goal 6 (SDG 6) is closely linked to the sustainable management of water resources and sanitation worldwide. SDG 6.4, in particular, aims to significantly improve water-use efficiency across all sectors by 2030. It is important to acknowledge the significant role that behavioural aspects of water users in an agricultural context play in contributing to water-use efficiency. This systematic review aims to provide an up-to-date synthesis of the current knowledge of water-use behaviours in agriculture to stay on track in achieving SDG 6. This systematic literature review investigates the factors influencing water-use behaviour among agricultural water users globally. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, we retrieved a total of 867 records, of which 47 matched the eligibility criteria. The 47 relevant studies were primarily conducted in the United States and China with key themes including sustainable agricultural practices, technology adoption for productivity, climate change adaptation, and modelling and uncertainty in water conservation. Additionally, the review identified six distinct water-use behaviours investigated from 2020 to 2024, which were each driven by its unique set of factors. Overall, the findings from the systematic review indicate that there has been a geographical gap in research efforts over the past five years, and it is important for water-use behaviour-related research to be extended to other countries that are water-stressed. Furthermore, the researchers recommend that future studies should develop comprehensive behavioural models and adopt holistic approaches to better understand and promote sustainable water-use behaviours in agriculture. These efforts are vital for achieving sustainable water management and broader SDGs.

In South Africa, the agricultural sector is a crucial pillar of the economy, with the livestock and grain industries playing significant roles in ensuring food security, fostering economic growth, and providing employment opportunities, particularly in rural areas. This research addresses the relatively unexplored relationship between the livestock and grain industries in South Africa. This study employs a comprehensive approach using a VAR/VECM framework alongside VECM Granger causality tests, Toda Yamamoto causality tests, impulse response functions, and variance decomposition analysis. The main findings of this study demonstrate a long-run relationship among the study variables, with consistently low error correction terms indicating slow short-term adjustments. Significant long-run relationships were observed between grain feed prices and livestock prices, where yellow maize and soybean prices affect live weaner prices, while beef carcass prices influence yellow maize prices. Overall, the results highlight the pivotal role that yellow maize plays as a link between the South African livestock and grain markets. The study concluded that policy formulation for the South African agricultural sector must consider the interconnected nature of the grain and livestock markets to achieve sustainable and effective outcomes.

Water use behaviour and efficiency are essential topics regarding water scarcity. Water is a life-sustaining resource used for various activities within the three primary sectors: agricultural, industrial, and domestic. Increasing competition among these sectors could affect the availability and sustainability of water use. The higher demand for agricultural-related commodities emphasizes the efficient and productive use of water. Still, to achieve this, the behaviour of consumers regarding water use needs to be changed. This systematic review paper aims to highlight the factors affecting water use behaviour and efficiency for agricultural production in South Africa. It further aims to determine how agricultural producers change their behaviour to improve their water use efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria were used as a reporting framework and guidelines to identify the articles included in the review. The review only included articles focussing on agricultural water use behaviour and efficiency and articles written in English and excluded articles from web pages, blogs, magazines, etc. The databases used for the review were Google Scholar and Web of Science. The articles were reviewed by the five authors to avoid the risk of bias, along with the inclusion and exclusion criteria. The final review included 30 peer-reviewed articles. A word frequency table was developed using the NVivo 14 software to conduct a thematic analysis for the review. The main factors which played a role in the water use behaviour and efficiency of farmers were (i) climate and adaptation strategies, (ii) policy and water pricing, and (iii) agricultural production and management. Each category elaborated on how water use could be improved and the different measures adopted to incorporate sustainable farm water use. This could be a guideline for farmers, stakeholders, and policymakers to improve and enhance water use behaviour and efficiency in Sub-Saharan Africa, particularly South Africa. This could ultimately assist in efficiently using the water while enhancing sustainability within the agricultural sector and attaining Sustainable Development Goal (SDG) six, which is to increase water use efficiency. The limitation of this study was that it was only narrowed down to the geographical context of South Africa. This review was funded by the Water Research Commission (WRC) of South Africa (Project Number: C20222023-00798). This review was not registered.

Grain marketing is complex because important decisions are made on the timing of sales and the quantities sold at every trading activity. The literature suggest various grain-hedging strategies, however these strategies are not adaptable to changing market conditions or are difficult for a producer to implement. To address these limitations, our study developed tailor-made moving average crossover (MAC) strategies that are adaptable to changing market conditions and can be easily followed by risk neutral and risk averse grain producers. The study used daily closing prices for the white maize May futures contract for the period 2009/2010 to 2019/2020. An optimization model was solved using the evolutionary algorithm embedded in Excel^® to identify the optimal MAC strategy that maximizes the margin above marketing cost for a risk aversion level. The results showed that optimal MAC strategies differ amongst producers with different levels of risk aversion. Furthermore, it was found that the risk-averse producers perform best by marketing their grain early in the marketing season. Meanwhile, the risk-neutral producers perform better by spreading their marketing activities throughout the season. The results further showed that the optimal MAC strategies performed better than the previously proposed routine strategies. The conclusion is therefore that an optimal MAC strategy outperforms routine strategies because of its ability to adapt to changing market conditions, while still being easy to implement.

Artificial intelligence (AI) is increasingly applied in agriculture to support data-driven decision-making, improve productivity, and enhance resource management. Small-scale farmers, who produce a significant share of the world’s food yet often operate under resource constraints, may particularly benefit from these technologies. However, it remains unclear how AI research addresses the needs of small-scale farming systems and the extent to which farmers directly interact with AI tools. This study conducts a systematic literature review to examine the applications, impacts, and challenges of AI in small-scale agriculture. The review followed the PRISMA 2020 guidelines and applied a structured review methodology, using the Web of Science, Scopus, and EBSCOhost databases. A total of 182 studies were identified and analyzed. The results show a rapid increase in publications after 2020, with research concentrated mainly in Africa and Asia. Most studies focus on technical AI applications such as plant disease detection, crop yield prediction, crop classification, and environmental monitoring, commonly using machine learning and deep learning techniques. However, only a small number of studies examine farmers’ direct interaction with AI systems, including adoption, perceptions, and practical usage. This imbalance indicates that the literature remains largely technology-driven rather than farmer-centred. The review highlights important research gaps, particularly in farmer engagement, integrated farm management applications, and the translation of AI prototypes into scalable solutions. Future research should prioritize participatory approaches and context-sensitive AI systems to ensure that technological advances effectively support small-scale farmers and sustainable agricultural development.

Agriculture is one of the largest consumers of water worldwide. To sustain these precious resources, effective water management is critical to achieving Sustainable Development Goal (SDG) 6 by 2030. To address the pressing challenges of water scarcity and increasing food demand, various water management strategies have been explored, focusing on technological innovations, policy frameworks, and economic incentives. Among these strategies, the adoption of water-saving technologies (WSTs) has gained particular prominence due to their potential to drastically reduce water usage in agriculture while maintaining crop productivity. However, the adoption rates of WSTs remain low in many agricultural contexts, highlighting the need to understand farmers’ behaviour and decision-making processes. This study aims to model the intentions of irrigators in adopting WSTs in the Orange Riet Irrigation Scheme of the Free State Province of South Africa using the Theory of Planned Behavior (TPB). The analysis employs partial least squares structural equation modelling (PLS-SEM) to investigate the factors influencing the adoption of WSTs among maize irrigators. The findings indicate that attitudes and subjective norms significantly influence irrigators’ intentions to adopt WSTs, while perceived behavioural control does not show a notable impact. These insights suggest that interventions should prioritize raising awareness about the benefits of WSTs, leveraging social networks, and addressing potential barriers such as high upfront costs and technical complexity. By understanding the factors driving WST adoption, targeted strategies (such as awareness campaigns, peer engagement, and reducing financial barriers) can be developed to enhance sustainable water management in similarly stressed environments and contributes to achieving the SDG2, African Union CAADP Strategy and Action Plan: 2026–2035 of ending hunger and poverty and intensifying sustainable food production, agro-industrialization, and trade, respectively.