The global water security situation is deteriorating due to unequal distribution of water resources and changing climate, leading to increased conflicts in many regions. This article proposes and develops a three-stage collaborative water resource allocation model and applies this to the Indus River basin in Pakistan, where water resources are shared by four provinces (agents): Punjab, Sindh, Balochistan, and Khyber Pakhtunkhwa (KPK). The model uses bankruptcy rules, Nash bargaining theory, and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to allocate water resources. The model considers various factors, such as water risk and water satisfaction to achieve the best possible (most equitable and acceptable) outcome. Water allocation was conducted under three scenarios of ‘median’, ‘maximum’ and ‘low’ river flows. In the first stage of water allocation, the positive and negative ideal solutions were defined for all agents (in this case, provinces). These initial ideal solutions provided a baseline for the negotiation process. In the second stage, water allocation ratios of the four provinces Punjab, Sindh, Balochistan and KPK, using the Nash bargaining solution, under the median flows were 57.61%, 29.91%, 6.24% and 6.24%. In the third stage, water allocation ratios demonstrated the reduction in the allocation for those provinces facing high risks and having high satisfaction rates. The final allocations under the median flow conditions for the four provinces were 54.92%, 28.95%, 8.50% and 7.63%, respectively. The developed three-stage water allocation model considers the multi-dimensional attributes of water resources and is expected to support the cooperation of water agents, enabling collective bargaining and group negotiation and improving the acceptability and stability of allocations.

The adoption of effective coping strategies is crucial for successful adaptation to the impacts of climate change in the dairy sector. However, little attention has been paid to understanding the perceived constraints and motivations toward such strategies. A survey was conducted among 104 dairy farmers from three semi-arid regions of South India. The aim of the survey was to explore the dairy farmers’ perception of climate risk, how it impacts their dairy farming system, the coping strategies they employ, and the barriers they face when implementing these strategies. The survey also investigated the factors that facilitate the adoption of adaptation measures. The results indicate dairy farmers in the region perceive drought, pests and diseases, and high temperatures as the major risks associated with climate change, which has resulted in decreased dairy income, animal health problems, reduced fertility, and food intake problems for their cattle. In response to climate variability, dairy farmers have adopted various coping strategies. The most important strategies include buying livestock insurance, keeping low debt obligations, and growing drought-tolerant grass varieties. However, most farmers face significant constraints in adopting these and other strategies including a lack of climate forecast data, the high cost of adaptation activities, and weak institutional support. On the other hand, the key enabling factors that support the adoption of these strategies include milk production security, suitable feed growing conditions, and family interest. Most importantly, the study found that certain factors such as age, education, number of earning family members, annual milk production, monthly cattle expenses, and landholdings significantly influenced dairy farmers’ strategies for adapting to climate change. The study recommends that providing timely climate forecasts, implementing improved policies such as vaccination and cattle health services, and establishing strong institutional support systems can help dairy farmers become more resilient to climate change and protect their livelihoods.

Indian farmers have been facing significant production risks that have substantially decreased their potential yield. To reduce these risks, farmers employ various tactical strategies. The choice of strategy, however, depends mainly on their risk attitude and perceptions. This study examines the factors influencing farmers’ risk attitude and perceptions based on the survey of 350 cotton farmers from Virudhunagar district in Tamil Nadu, India. Risk attitude is evaluated using a Multiple Price List (MPL) experimental method, while the risk matrix is utilised to measure the risk perception of drought, uneven rainfall distribution, and pests and diseases. Logit model is applied to assess the variables associated with farmers’ risk attitude and perceptions. The findings reveal that 75% of the respondents exhibit risk-averse behaviour, while only 12% display a risk-seeking attitude. The majority of respondents identify drought and rainfall as major risks compared to pests and diseases. Logit model results show that gender, education, organisational membership, irrigation access, farming experience, access to credit, contact with extension personnel, and yield loss negatively influence farmers’ risk attitude. Similarly, organisational membership, a higher share of non-farm income, more farm size, irrigation availability, and credit accessibility reduce the farmers’ risk perceptions. These findings help policymakers understand how local farmers perceive farm risks such as drought, rainfall variations, and pests and diseases and consider these viewpoints when developing sustainable adaptation measures. This study highlights the significance of farmer group organisations, improved extension services, and credit access in shaping farmers’ risk attitude and perceptions, thereby enhancing farm productivity.

In making irrigation decisions, farmers typically rely on local observation and experience, such as observing crops and neighbors’ actions. Research has mainly focused on understanding crop water requirements to improve farming practices, but it is important to acknowledge that farmers have unique perspectives, access to diverse local “signals”, and experience. The challenge is to strike a balance between complex technical assessments of field conditions (the science) and harnessing farmers’ skills to manage their irrigation in ways that maximize yield and quality. This study established a basis for specifying minimum data requirements for pragmatic, but not necessarily perfect, irrigation decision-making for small-scale Vietnamese coffee farmers. This study focuses on three areas in Dak Lak province in the Central Highlands of Vietnam. To explore the role of monitoring in irrigation management, two contrasting monitoring systems were set up to collect soil, weather, and irrigation data. We also compared a variety of water balance models with different data requirements, with a focus on processes that used “passive data collection”, i.e., farmers do not manually collect data, rather data can be accessed readily from external sources. In Vietnam, traditional hosepipe irrigation is applied where it is impractical to know the volume of applied water. The proposed Low Data Model (LDM) is suited to more informed irrigation scheduling decisions, which have potential to improve the likelihood of coffee growers adopting measurement-based decision-making. While researchers may seek a detailed daily sub-millimeter understanding of soil water dynamics, farmers require practical decision support if there is to be any adoption of improved methods. This study offers a simple and practical approach for irrigation scheduling rather than a model using numerically perfect data that is unachievable in the field. The work demonstrates that on-site rainfall data is essential. However, other data can be collected passively to reduce the burden of data collection on users. This approach may enhance the likelihood of model-based irrigation scheduling being adopted by coffee farmers in Vietnam.

Protracted droughts, defined as successive dry events occurring before full vegetation recovery, pose an emerging challenge to assessing ecosystem resilience. Conventional drought analyses often treat these events as isolated, overlooking the temporal dependencies that govern vegetation stress. This study addresses this limitation by proposing an integrated spatiotemporal drought assessment approach that links Standardised Precipitation Index (SPI) based identification of individual drought events with vegetation recovery dynamics derived from Normalised Difference Vegetation Index (NDVI) and Vapour Pressure Deficit (VPD), enabling the identification of protracted droughts across Queensland, Australia. Results reveal a noticeable seasonal pattern, with NDVI consistently decreasing following drought periods (SPI-1 < -1) and showing a negative-lagged relationship with VPD, indicating delayed vegetation recovery. The mean recovery duration across Queensland was estimated at 3.38 months, quantified as the time lag between drought termination (SPI ≥ -0.5) and the return of NDVI to baseline conditions. Based on this definition, region-specific recovery lags were derived and used to generate ground-truth labels for protracted drought events. Spatiotemporal drought patterns were analysed using a hybrid deep learning framework integrating a convolutional neural network (CNN) and recurrent layers, including long short-term memory (LSTM) and bidirectional-LSTM (BiLSTM), with and without multi-head attention (MHA). The CNN-LSTM-MHA model achieved the highest performance (accuracy 95.39% and F1-score 0.9569) in detecting protracted drought events across space and time, demonstrating the significance of integrating temporal memory with attention mechanisms. This study shows that coupling remote sensing indices with deep spatiotemporal learning enhances protracted drought detection by explicitly modelling vegetation recovery-dependent temporal dependencies, capturing lagged NDVI responses to atmospheric dryness and enabling more consistent identification of successive drought events before full recovery, thereby improving the interpretation of vegetation resilience and supporting adaptive management in climate-sensitive regions.