Climate Change Intensification Research Articles

3D visualization of hurricane storm surge impact on urban infrastructure

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

From Green to Black: A Voluminous Political Ecology of the Extraction–Conservation Nexus

Within political ecology research, a dominant focus on the hard physicality of the world limits engagement with how events taking place on land mediate and are mediated by other material spaces like the atmosphere. This article engages with burgeoning research on the extraction–conservation nexus to show how the clearly demarcated land-based boundaries on which nexus thinking relies limit an awareness of how processes of conservation and extraction cohere and take shape in and through the aerial atmosphere. The article substantiates this argument with case studies on Guyana and Suriname, two countries that have been working on avoiding deforestation through Reducing Emissions from Deforestation and Forest Degradation for over a decade in the aim of mitigating climate change. In each case, we examine three years of news reporting on recent, major oil finds in the Guyana-Suriname Basin. The news reports, set against longer term research, demonstrate a narrative pivot from “green,” land-based avoided deforestation narratives to “black,” offshore extractive ones. The reports show that reference to the competing atmospheric effects of the mutual pursuit of these activities is scarce, even at a time of rapidly intensifying climate change. Hence, we argue that a voluminous analysis of the extraction–conservation nexus integrating a vertical awareness of the ever-present and unbounded atmosphere harbors potential for orienting a less contradictory politics of climate change—one that recognizes how activities deemed oppositional on land take shape in the shared, unbounded atmosphere. These activities consequently go on to affect other spaces and places in indirect, often unpredictable ways.

Projecting the future redistribution of Pinus koraiensis (Pinaceae: Pinoideae: Pinus) in China using machine learning

IntroductionAs an important coniferous tree in northeast China, Pinus koraiensis not only maintains the stability of the forest ecosystem at high latitudes but also plays a crucial role in regional socioeconomic development. With the intensification of climate change in recent years, the stability of P. koraiensis habitats is constantly disturbed by external uncertain environmental factors, which greatly affects the geographical distribution of P. koraiensis. However, its geographical distribution is still unclear, which greatly hinders further understanding of the ecological process of P. koraiensis. Consequently, it is particularly important to explore the potential distribution and migration of P. koraiensis during several critical periods.MethodsRandom forest (RF) was used to establish the redistribution of P. koraiensis.ResultsThe results showed that temperature seasonality and precipitation in the coldest quarter were the key factors limiting the current distribution of P. koraiensis. Currently, P. koraiensis is mainly distributed in the Lesser Khingan Mountains and Changbai Mountains, with a total suitable area of ~4.59 × 105 km2. In the past, the historical distribution of P. koraiensis during the LIG period was basically consistent with the current distribution range, but its distribution range was more complete. In the LGM period, the suitable distribution of P. koraiensis became fragmented, especially at the connection between the Lesser Khingan Mountains and the Changbai Mountains. Under future climate scenarios, the suitable distribution of P. koraiensis is projected to increase, while the highly suitable distribution will be reduced. The dramatically worrying change is that the suitable habitats of P. koraiensis are gradually breaking and separating in the junction zone between the Lesser Khingan Mountains and Changbai Mountains, which will cause the ecological corridor to break. The shifts in the distribution centroid indicated that the P. koraiensis population will migrate northward.DiscussionHowever, it remains to be verified whether long-distance migration can be achieved without human assistance. Our results can provide some solutions for protection and management strategies for P. koraiensis populations and the impact of climate change, shedding light on the effectiveness of management responses.

Research on Drought Monitoring Based on Deep Learning: A Case Study of the Huang-Huai-Hai Region in China

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions.

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

AbstractPlant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21st century. To address this challenge, we conducted linked yield trials of 752 public maize (Zea mays) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

Evaluation of a heating protocol and stocking density impact on heatstressed fattening pigs

As climate change intensifies, heat stress mitigation for pigs becomes more important. Trials involving induced heat waves are useful to test several measures (e.g. reduced stocking density) at a faster rate, but only when accurately evaluated and validated. In the present study, we investigated the suitability of an artificial heating protocol at different pig weights (experiment 1). The impact of different stocking densities on fattening pigs during an artificial heat wave (experiment 2) was also investigated. Experiment 1: Forty 20-week-old pigs weighing 96.5 ± 7.3 kg (W100) and forty 17-week-old pigs weighing 72.7 ± 9.9 kg (W70) were housed in two compartments. An artificial heat wave (heat load) was induced for 3 days. During 3-day periods before, during and after the heat load, physiological parameters (respiration rate (RR), rectal temperature (Trectal), skin temperature (Tskin) and behavior) were measured and average daily feed intake was observed. Ambient temperature, relative humidity and temperature-humidity index (THI) were monitored. Experiment 2: A total of 150 fattening pigs were randomly divided into three treatment groups: SD1.3 (1.3 m2/pig), SD1.0 (1.0 m2/pig) and SD0.8 (0.8 m2/pig). All pens had a total pen surface of 4.88 m2, corresponding with 4, 5 and 6 fattening pigs in the SD1.3, SD1.0 and SD0.8 groups, respectively. The heat load was induced for 7 days on week 21. Respiration rate and Trectal were observed as in experiment 1. Average daily gain and average daily feed intake were also noted. During the heat load, THI reached ≥ 75 (78.4 (experiment 1) and 78.6 (experiment 2)), even when relative humidity decreased to ± 45%. Every physiological parameter showed significant increases during the heat load. The prolonged heating protocol in experiment 2 also provoked significant decreases in average daily feed intake (15%) and average daily gain (19%) for all groups. Weight within the studied range of 70–100 kg did not have a significant impact on any of the parameters. However, Tskin was affected by both weight and heat load (P < 0.05), where Tskin from W100 was always lower in comparison to W70. In addition, we found that 0.8 m2/pig doubled the increase of Trectal during the heat load, namely SD0.8 (0.22 °C) compared to SD1.0 (0.12 °C) (P = 0.033) and SD1.3 (0.13 °C) (P = 0.053). This suggests that pigs housed at higher densities are less able to regulate their internal heat production. However, RR and performances were not significantly affected by heat load in this experimental set-up. A stocking density of 1.0 m2/animal may be sufficient to mitigate some negative effects of heat stress.

Community Service Perspectives on Climate Change and Social Justice in Western Australia

ABSTRACT Social workers increasingly recognise the relationship between social injustice and climate change, which is underpinned by oppressive systems of power such as colonialism, racism, capitalism, and patriarchy. Climate change exacerbates existing structural inequities, with disproportionately negative impacts on marginalised populations. In their work across diverse fields of social work practice, community service organisations (CSOs) have an important frontline role in supporting communities to mitigate and adapt to climate change. In the state of Western Australia (WA), a group of CSO leaders and university researchers commenced a feminist participatory action research (FPAR) program about climate justice and community services. In the first FPAR cycle, they undertook a survey, interviews, and workshops with CSO workers to examine social justice impacts of climate change in WA. CSO workers generally self-reported a low understanding of social justice and climate change. They identified climate impacts across themes of health, economic injustices, discrimination and access, housing and displacement, and gendered injustices. The authors contend that as climate change intensifies, CSOs must embed climate justice in their operations and services so that they might adequately prepare for, and respond to, the social justice impacts of climate change. IMPLICATIONS Increased understanding of workers’ perspectives of the social justice impacts of climate change in Western Australia can identify knowledge gaps in the community service sector. Embedding a climate justice lens can assist CSO operations and programs to prepare for, and respond to, the social justice impacts of climate change.

Decentralising Climate Governance in the Global South: Lessons from Itezhi-Tezhi and the Kafue Wetlands, Zambia

This article discusses the challenges of decentralising climate governance in the Global South in the context of intensifying climate change. Using qualitative methods of data collection and analysis, we interviewed a total of 112 participants who participated in the study, including traditional leaders, ward councillors, subject-matter experts, government officials and members of the public who took part in focus group discussions. Our results show that devolution, delegation, deconcentration and privatisation have been used as strategies for decentralising climate action in the Itezhi-Tezhi District. However, these methods of decentralisation are affected by several factors, including unclear rules for elected and appointed leaders, which influence the devolution of decision-making responsibilities at the local government level. Regarding delegation, decentralising climate governance is affected by inadequate public participation, particularly in the design and execution of climate initiatives in the Kafue wetlands. Furthermore, this article shows that deconcentration is likely to affect climate governance because the national government has not deconcentrated the units for climate governance from the line ministry to the local authority. Privatisation, which has been an effective way of decentralising climate governance in the Kafue wetlands, has also been problematic due to inadequate community participation. These challenges and opportunities serve as lessons for building resilience in effective decentralised climate governance in the Kafue wetlands. This article adds to the ongoing discourse on the efficacy of decentralising climate governance in the Global South and offers valuable insights for future research in this genre of academic inquiry in Sub-Saharan Africa.

Climate risk assessment and adaption ability in China's coastal urban agglomerations - A case study of Guangdong-Hong Kong-Macao greater bay area

As climate change intensifies, extreme weather events, especially the coastal flooding is becoming increasingly severe. Carbon emissions exacerbate climate change, leading to increased frequency and intensity of extreme precipitation, which may result in flooding. This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a coastal city cluster in China, and adopts the scenario analysis approach to evaluate flood risks in the different carbon mitigation scenario from the perspectives of dangerousness, and adaption ability. The result shows that the Huizhou in the east of the GBA and the Zhaoqing in the northwest have the highest level of flood disaster risk. However, the central region of the GBA (Guangzhou, Dongguan, Zhongshan, and Foshan) is very vulnerable to disaster-inducing environments and hazard-affected bodies, which means that the flooding risk and the impact of such risk in the GBA display regional differentiation characteristics and the spatial patterns of them are different. According to the carbon neutrality scenario, the flood risk in the GBA peaks in 2030, and decreases during 2030–2090. In contrast, under the uneven development and high carbon pathways, the risk peaks in 2060, and the overall risk by 2090 is roughly the same as that in 2060. In addition, the regions with the highest flood disaster risk are Guangzhou, Foshan, Dongguan and Zhongshan along the Pearl River estuary. Overall, it shows that areas with higher levels of urbanization and more developed economies will face higher flooding risks. The “dangerousness-adaption ability” structure can reflect the spatial distribution pattern of flood disaster risks, identify the priority areas for flood prevention and control, and is conducive to risk management. At the same time, the research results show that ecological protection have a positive effect on reducing flood risk. In addition, corresponding policy recommendations for pre-disaster, mid-disaster and post-disaster have been proposed, providing references for flood risk management in the GBA.

Extreme temperatures and school performance of the poor: Evidence from Mexico

As the risks associated with climate change intensify, understanding its impacts on human capital development is crucial. In this paper, we analyze the causal effects of temperature on the academic performance of students in Mexico, a middle-income country facing significant climate risks and socioeconomic challenges. Using panel data on over 5.5 million students, our results show that a 1 °C increase in annual average temperature leads to a 0.07 and 0.08 standard deviation decrease in Spanish and math test scores, respectively. Moreover, a one standard deviation (0.93 °C) increase in the long-term municipal temperature average is associated with 0.04 and 0.03 standard deviation declines in those scores. The effects are context-dependent - students in historically colder municipalities actually benefit from hotter temperatures, likely due to improved learning conditions in under-insulated schools and homes. However, the detrimental impacts appear consistent across urban, rural, and socioeconomically disadvantaged areas, underscoring the vulnerability of marginalized populations to the academic consequences of climate change. Overall, our findings highlight the urgency of addressing the educational dimensions of the global climate crisis through targeted interventions and adaptive policies, particularly in low-and middle-income countries.

Development beyond 2030: more collaboration, less competition?

The Sustainable Development Goals (SDGs) represented a key landmark in collaboration and shared agenda-setting to address global challenges across scales and geographies. However, despite initial optimism that measurable goals would support accountability and transparency in development, progress towards realising goals has been mixed. Global development agendas increasingly face challenges from the intensification of climate change, the return of populism and ethnonationalism, and a deepening of inequalities at intra- and inter-national scales. This article interrogates the priorities that must inform a critical post-SDG development agenda. To think towards this, we first explore three questions of the development agenda: 1) can development be sustainable? 2) Can development be delivered through markets? And 3) can development be ‘global’? To address these tensions and take a first step towards a more critical post-2030 agenda, we call for a focus on spatialities, multiplicities and historicities of development.

Evaluating Future Building Energy Efficiency and Environmental Sustainability with PCM integration in Building Envelope

The energy demand in the building sector is anticipated to increase with climate change and the high energy consumption is responsible for releasing enormous amounts of CO2 into the environment, intensifying climate change. Phase change material (PCM) integration into the building envelope has a high potential to reduce the building's energy demand without compromising environmental sustainability. This research investigated the impact of PCMs (PCM 18–PCM 30) on building cooling, heating, and annual energy efficiency for future climate scenarios (2095) in 13 different climate zones worldwide. For this purpose, the Fanger comfort model was implemented and numerical simulations were performed using Energyplus. The performance of PCM was evaluated using novel Predicted Mean Vote fluctuation reductions (PMVFR), and the Total Predicted Mean Vote drop (TPMVD). Further, for the first time, a time series-forecasting model for the estimation of CO2 emissions originating from various energy production sources in the future was developed using gene-expression programming (GEP) algorithm. Finally, the two different maps showing the PCM efficiency for energy conservation and environmental sustainability at the global level are presented. The results evidence a maximum of 12.9% reduction in annual energy demand in the future with the integration of PCM. The GEP-based prediction model demonstrated good accuracy as determined by the statistical parameters and satisfied the external validation requirements. The environmental analysis exhibits that PCM integration can reduce carbon emissions up to 204 CO2-e Kg/year. Overall, the integration of PCM is regarded as a sustainable solution for building energy efficiency, considering climate change.

Nepali Army in Climate-Induced Security Challenges

In the interconnected world today, the connection between climate change, peace, and security has gained significant recognition. Security actors, including the armed forces, are increasingly confronted with extreme weather events demanding for assistance both at home and abroad. Their military assets and ability to operate are affected by changing weather patterns, rising sea levels, and other climate impacts. Climate change is transforming the way we think about security. As climate change intensifies, its impacts risk exacerbating existing social, economic, and environmental drivers of insecurity at local levels (where communities struggle to cope with converging pressures) and globally (when resources and ecosystems are shared across national borders). Climate insecurity hinders the adaptation efforts, and risks leaving already vulnerable communities even poorer and less resilient to interlinked climate and security crises. As the global temperature is rising, climate and disaster risks in Nepal are affecting people, the environment, the economy, development gains, and overall security of the country. This article begins by briefly highlighting the climate-induced insecurity to Nepal and efforts made to minimize its impacts. Then the article endeavors to present a comparative overview of the leading greenhouse gas (GHG) emitting nations including Nepal followed by the analytical examination of climate change impacts on states and armed forces. Finally, the article concludes by presenting suitable recommendations to the NA in responding the climate-induced security challenges. The article is subjectively prepared by analytically reviewing various literatures on climate-induced security challenges in the world today.

Study on the Influence of the Application of Phase Change Material on Residential Energy Consumption in Cold Regions of China

As the impact of climate change intensifies, meeting the energy demand of buildings in China’s cold regions is becoming increasingly challenging, particularly in terms of cooling energy consumption. The effectiveness of integrating phase change material (PCM) into building envelopes for energy saving in China’s cold regions is unclear. The aim of this study is to assess the effectiveness of PCM integration in building enclosures for energy efficiency in these regions. The research monitored and recorded indoor temperature data from typical residential cases from May to September. This measured data was then used to validate the accuracy of EnergyPlus22-1 software simulation models. Subsequently, the calibrated model was utilized to conduct a comparative analysis on the effects of PCM on indoor temperatures and cooling energy consumption across these regions. The results of these comparative analyses indicated that PCM can alleviate indoor overheating to varying degrees in severe cold regions of China. Focusing on north-facing bedrooms, applying PCMs reduced the duration of overheating in non-air-conditioned buildings in severe cold regions of China by 136 h (Yichun), 340 h (Harbin), 356 h (Shenyang), and 153 h (Dalian). In terms of cooling energy consumption, the energy saved by applying PCMs ranged from 1.48 to 13.83 kWh/m2. These results emphasize that the performance of PCM varies with climate change, with the most significant energy-saving effects observed in severe cold regions. In north-facing bedrooms in Harbin, the energy-saving rate was as high as 60.30%. Based on these results, the study offers guidance and recommendations for feasible passive energy-saving strategies for buildings in severe cold and cold regions of China in the face of climate change. Additionally, it provides practical guidance for applying PCMs in different climatic zones in China.

Biodiversity loss and inter-provincial cooperative protection in China based on input-output model

Along with habitat destruction and the intensification of climate change, economic and social development is threatening the living of species. In the context of China's domestic economic circulation, the spatial structure of biodiversity loss is complicated by the decentralization of production, with many regions affecting the survival of species in other regions. Based on the environmentally extended MRIO model, this paper calculated the subnational biodiversity loss footprint through structural path analysis and social network analysis, identified the regional linkages and spatial networks of species loss, and traced the conservation responsibility to specific sectors. Our findings indicate that Yunnan, Tibet, Guangdong, and Sichuan possess the most significant biodiversity footprints, comprising over 40% of the total footprint. In terms of threat patterns, Yunnan and Tibet have the largest intra-regional footprints, indicating their important impact on species within the region; Guangdong and Zhejiang have the largest extra-regional footprints, indicating their role in threatening other species through cross-regional trades. The primary industry, tap water and food and tobacco sectors are major biodiversity footprint producers. Overall, some eastern and coastal areas are the final drivers of biodiversity loss in China, driven by increasingly complex trade links. Notably, the directionality of species loss in biodiversity hotspots is evident, with Shandong and Guangdong significantly influencing the biodiversity of Yunnan and Guangxi, respectively. This study systematically analyzed the complete network of biodiversity loss and divided four biodiversity protection communities to promote the realization of trans-regional protection and compensation. Examining the biodiversity loss through the lens of networks, and connecting economic activities to species, serves as a warning to decision-making bodies that a future of increasingly intricate domestic trade could pose a critical threat to biodiversity, necessitating collaborative conservation efforts.

Carbon reduction attention and financial market stress: A network spillover analysis based on quantile VAR modeling

As climate change intensifies, attention to the issue of carbon emission reduction has gradually increased. This research constructs a complete set of indicators of carbon reduction attention and financial market stress and applies the quantile VAR method to calculate the volatility spillover between carbon reduction attention and financial market stress. We conclude with the following. Firstly, a relatively close volatility spillover association exists between carbon reduction attention and financial market stress. In the research system, carbon reduction attention mainly assumes the role of information receiver. Additionally, when examining the spillover status in different quantiles, the total spillover level shows an irregular "bowl" structure, while the net spillover level of each variable has different shapes. Secondly, the dynamic spillover level in the extreme quantile condition maintains a connectivity range of 60–80%, significantly higher than that of the median condition. Finally, this study finds two sets of significant complementary spillovers within the system, namely, "carbon reduction attention - crude oil market stress" and "stock market stress - real estate market stress", which provide investors with an opportunity to explore the potential of the carbon reduction attention and real estate market stress in the future.

Soybean Seed Coat Cracks and Green Seeds—Predisposing Conditions, Identification and Management

Seed coat cracking and green seeds threaten soybean crop production. Seed coat cracking results from a complex interplay of genetic factors, environmental stresses, and crop management practices. Green seeds, linked to water deficit, nutritional deficiencies, and environmental stresses, exhibit reduced quality and viability. The intricate relationships between seed coat integrity and seed permeability, influenced by the lignin content, porosity, and color, play a pivotal role in seed germination, storage potential, and resistance to field stresses. These issues reverberate through the soybean agricultural supply chain. Strategic interventions are crucial to address these abnormalities and ensure soybean productivity. Seed germination and vigor are reduced due to seed coat cracking and green seeds, undermining food security and necessitating additional resources for disease management. The occurrence and identification of green seeds and seeds with cracks in the seed coat were also reported by identifying the genes and QTLs (quantitative trait loci) associated with these characteristics. Herbicides, commonly used in weed management, may offer a strategic approach to mitigating seed coat cracking and green seed occurrence. Understanding the complex interactions between the genetics, environmental factors, and management practices influencing seed abnormalities is essential as global climate change intensifies. This review emphasizes the need for integrated strategies, balanced plant nutrition, and cohesive phytosanitary management to mainly alleviate seed coat cracking and greenish occurrences in soybeans and other plant species.

Determination of crop water requirements and potential evapotranspiration for sustainable coffee farming in response to future climate change scenarios

Climate change (CC) is causing a significant threat to agriculture, a sector complicatedly tied to natural resources. Changes in precipitation patterns, atmospheric water content, and rising temperatures intensely affect global agriculture, especially in tropical regions. In this intense CC scenario, potential evapotranspiration (PET) and crop water requirement (CWR) are critical components of agricultural water management. This study evaluates the future impact of CC on precipitation, CWR, and PET in different provinces of Thailand's northern and northeastern regions. Three bias correction methods (Delta (DT), Empirical Quantile Mapping (EQM), and Quantile Mapping (QM)) were employed for precipitation downscaling from the CanESM5 CMIP6-GCM across selected 13 coffee farms with different coffee species. Arabica and Robusta coffee were carefully selected for this analysis. The DT method demonstrated superiority, exhibiting lower RMSE and higher correlation coefficients than EQM and QM. Farm-specific assessments illuminated water demand's complex dynamics during critical growth stages, showcasing variable CWR and PET. During the blooming stage in N-F1, CWR ranged from 16.7 to 33.7 mm/stage, highlighting the variability in water needs. Projected CC impacts on Arabica and Robusta coffee farms in Chiang Rai and Sisaket presented challenges, emphasizing farm-specific strategies to address potential water deficits or surpluses during critical growth phases. Projected 2023, 2028, and 2033 precipitation demonstrated incongruities with CWR and PET. The findings emphasize the crucial role of farm-specific adaptive strategies in mitigating the impacts of changing precipitation patterns on coffee cultivation.

Assessing Carbon Emissions from Animal Husbandry in China: Trends, Regional Variations and Mitigation Strategies

The intensification of land use and the accelerated integration of three industries (agricultural production, agricultural product processing industry, and agricultural product market service industry) in China have resulted in the continuous expansion of animal husbandry and its industrial chain. This phenomenon has led to a rise in greenhouse gas (GHG) emissions from livestock farming, intensifying climate change and placing strain on worldwide environmental conservation efforts. Life cycle assessment (LCA) was utilized to evaluate carbon emissions from China’s animal husbandry sector from 2012 to 2021. Additionally, the logarithmic mean divisia index (LMDI) decomposition method was employed to examine and elucidate the influential impacts of five factors on carbon emissions. These factors included the efficiency of animal husbandry production, the structure of the agricultural industry, per capita agricultural production income, urbanization, and the total population. The results reveal the following: (1) From 2012 to 2021, China’s animal husbandry sector witnessed a marginal increase in cumulative carbon emissions from 287.74 million tons to 294.73 million tons, with an annual growth rate of 0.42%. (2) Emission contributions were categorized as follows: the production stage (149.61 million tons), the transportation stage (145.07 million tons), and the processing stage (0.05 million tons). (3) The primary factor contributing to the rise in carbon emissions from animal husbandry from 2012 to 2021 was the per capita agricultural production income factor (A3), alongside a notable impact from the total population factor (A5).

Nutrient content in olive leaves through sustained irrigation with treated wastewater

Water scarcity is posing a major challenge to sustainable development. This issue will become more pressing as the global population grows, living standards rise, food habits evolve, and the effects of climate change intensify, resulting in rising temperatures globally. Reusing treated wastewater reduces effluent discharge into natural water bodies while also providing a reliable water source for applications that do not require high-quality water. A field trial was conducted on plots where olive trees grow. Two water treatment scenarios were investigated: one with a conventional water source as the control and one with treated wastewater as an alternative.Soil and irrigation water were characterized in the study regions, and chlorophyll, macro and micronutrients in olive leaves were analyzed. Soils treated to long-term wastewater irrigation had considerably higher electrical conductivity, total nitrogen, potassium, sodium, magnesium, zinc, and cation exchange capacity values than control. Notably, control soils had much greater levels of zinc and phosphorus than treated wastewater-irrigated soils. Except for phosphorous content (greater in plants from treated wastewater irrigated plots) and zinc content (lower in plants from treated wastewater irrigated plots), no significant variations in nutrients evaluated occurred for crops. In general, when comparing irrigation with reclaimed wastewater to irrigation with fresh water, no negative influence on tree performance was detected over time. Nevertheless, it's crucial to consider the potential risk of soil salinization associated with the consistent use of this water for irrigation.