Climatic and non-climatic stressors, such as temperature increases, rainfall fluctuations, population growth and migration, pollution, land-use changes and inadequate gender-specific strategies, are major challenges to coastal agricultural sustainability. In this paper, we discuss all pertinent issues related to the sustainability of coastal agriculture under climate change. It is evident that some climate-change-related impacts (e.g., temperature and rainfall) on agriculture are similarly applicable to both coastal and non-coastal settings, but there are other factors (e.g., inundation, seawater intrusion, soil salinity and tropical cyclones) that particularly impact coastal agricultural sustainability. Coastal agriculture is characterised by low-lying and saline-prone soils where spatial competition with urban growth is an ever-increasing problem. We highlight how coastal agricultural viability could be sustained through blending farmer perceptions, adaptation options, gender-specific participation and integrated coastal resource management into policy ratification. This paper provides important aspects of the coastal agricultural sustainability, and it can be an inspiration for further research and coastal agrarian planning.

Regenerative agriculture (RA) is an approach to farming pursued globally for sustaining agricultural production and improving ecosystem services and environmental benefits. However, the lack of a standardized definition and limited bioeconomic assessments hinder the understanding and application of RA more broadly. An initial systematic review revealed a wide range of definitions for regenerative agriculture, although it is generally understood as a framework consisting of principles, practices, or outcomes aimed at improving soil health, biodiversity, climate resilience, and ecosystem function. To address existing gaps, we propose a working definition that integrates socioeconomic outcomes and acknowledges the significance of local knowledge and context to complement established scientific knowledge. A second systematic review identified indicators, tools, and models for assessing biophysical and economic aspects of RA. Additionally, a third literature review aimed to identify the potential integration of advanced analytical methods into future assessments, including artificial intelligence and machine learning. Finally, as a case study, we developed a conceptual framework for the evaluation of the bioeconomic outcomes of RA in the mixed farming setting in Australia. This framework advocates a transdisciplinary approach, promoting a comprehensive assessment of RA outcomes through collaboration, integrated data, holistic frameworks, and stakeholder engagement. By defining, evaluating assessment methods, and proposing a pragmatic framework, this review advances the understanding of RA and guides future research to assess the fit of RA practices to defined contexts.

Even though climate change is having an increasing impact on tea plants, systematic reviews on the impact of climate change on the tea system are scarce. This review was undertaken to assess and synthesize the knowledge around the impacts of current and future climate on yield, quality, and climate suitability for tea; the historical roots and the most influential papers on the aforementioned topics; and the key adaptation and mitigation strategies that are practiced in tea fields. Our findings show that a large number of studies have focused on the impact of climate change on tea quality, followed by tea yield, while a smaller number of studies have concentrated on climate suitability. Three pronounced reference peaks found in Reference Publication Year Spectroscopy (RYPS) represent the most significant papers associated with the yield, quality, and climate suitability for tea. Tea yield increases with elevated CO₂ levels, but this increment could be substantially affected by an increasing temperature. Other climatic factors are uneven rainfall, extreme weather events, and climate-driven abiotic stressors. An altered climate presents both advantages and disadvantages for tea quality due to the uncertainty of the concentrations of biochemicals in tea leaves. Climate change creates losses, gains, and shifts of climate suitability for tea habitats. Further studies are required in order to fill the knowledge gaps identified through the present review, such as an investigation of the interaction between the tea plant and multiple environmental factors that mimic real-world conditions and then studies on its impact on the tea system, as well as the design of ensemble modeling approaches to predict climate suitability for tea. Finally, we outline multifaceted and evidence-based adaptive and mitigation strategies that can be implemented in tea fields to alleviate the undesirable impacts of climate change.

The potential land suitability assessment for tea is a crucial step in determining the environmental limits of sustainable tea production. The aim of this study was to assess land suitability to determine suitable agricultural land for tea crops in Sri Lanka. Climatic, topographical and soil factors assumed to influence land use were assembled and the weights of their respective contributions to land suitability for tea were assessed using the Analytical Hierarchical Process (AHP) and the Decision-Making Trail and Evaluation Laboratory (DEMATEL) model. Subsequently, all the factors were integrated to generate the potential land suitability map. The results showed that the largest part of the land in Sri Lanka was occupied by low suitability class (42.1%) and 28.5% registered an unsuitable land cover. Furthermore, 12.4% was moderately suitable, 13.9% was highly suitable and 2.5% was very highly suitable for tea cultivation. The highest proportion of “very highly suitable” areas were recorded in the Nuwara Eliya District, which accounted for 29.50% of the highest category. The model validation results showed that 92.46% of the combined “highly suitable” and “very highly suitable” modelled classes are actual current tea-growing areas, showing the overall robustness of this model and the weightings applied. This result is significant in that it provides effective approaches to enhance land-use efficiency and better management of tea production.

Banana is one of the main fruit crops in the world as it is a rich source of nutrients and has recently become popular for its fibre, particularly as a raw material in many industries. Mathematical models are crucial for strategic and forecasting applications; however, models related to the banana crop are less common, and reviews on previous modelling efforts are scarce, emphasizing the need for evidence-based studies on this topic. Therefore, we reviewed 75 full-text articles published between 1985 and 2021 for information on mathematical models related to banana growth and, fruit and fibre yield. We analysed results in order to provide a descriptive synthesis of selected studies. According to the co-occurrence analysis, most studies were conducted on the mathematical modelling of banana fruit production. Modellers often used multiple linear regression models to estimate banana plant growth and fruit yield. Existing models incorporate a range of predictor variables, growth conditions, varieties, modelling approaches and evaluation methods, which limits comparative evaluation and selection of the best model. However, the banana process-based simulation model ‘SIMBA’ and artificial neural network have proven their robust applicability to estimate banana plant growth. This review shows that there is insufficient information on mathematical models related to banana fibre yield. This review could aid stakeholders in identifying the strengths and limitations of existing models, as well as providing insight on how to build novel and reliable banana crop-related mathematical models.

The threat of accelerating climate change on species distribution now and in the future is a topic of increasing research interest. However, little work has been undertaken to assess how shifting climates will affect the suitability of tea cultivation. Therefore, we used MaxEnt modelling to project the impact of current and future climatic scenarios on the potential distribution of tea across the four tea-producing countries of China, India, Kenya and Sri Lanka. Projections were made for the years 2050 and 2070 with three Representative Concentration Pathways (RCPs) using seven bioclimatic predictors under three global circulation models (GCMs). The current and future habitat suitability for tea predicted by the models produced a high accuracy rate, with high areas under the receiver operating characteristic curve (AUCs) for all tested RCPs under the three GCMs for the four countries. The mean true skill statistic (TSS) values for tea in Sri Lanka, Kenya, India and China were 0.80, 0.91, 0.91, and 0.74, respectively. The kappa values (k) of the current and future models for all four countries ranged from 0.40 to 0.75, which indicates that the overall performance of the model was good. The precipitation seasonality and annual precipitation were found to be the most influential variables in Sri Lanka and India, respectively, while annual mean temperature was the most effective contributor for determining the suitability of habitat for tea in Kenya and China. An important proviso is that some existing tea-growing areas will face reduced suitability for future tea cultivation suggesting that by 2050 there will be a drastic reduction in the optimal suitability by averages of 26.2%, 14%, and 4.7% in Kenya, Sri Lanka and China, respectively. The optimal suitability will be reduced by 15.1%, 28.6% and 2.6% in Kenya, Sri Lanka and China, respectively, by 2070. India displays an advantage in projected future climates as it gains optimal suitability areas of 15% by 2050 and 25% by 2070.