Search Results (441)

Ruminant livestock production plays a crucial role in the agricultural systems of Sub-Saharan Africa, significantly supporting rural livelihoods through income generation, improved nutrition, and employment opportunities. Despite its importance, the sector continues to face substantial challenges, such as low feed quality, seasonal feed shortages, and climate-related stresses, all of which limit productivity and sustainability. Considering these challenges, the adoption of natural feed additives has emerged as a promising strategy to enhance animal performance, optimise nutrient utilisation, and mitigate environmental impacts, including the reduction of enteric methane emissions. This review underscores the significant potential of natural feed additives such as plant extracts, essential oils, probiotics, and mineral-based supplements such as fossil shell flour as sustainable alternatives to conventional growth promoters in ruminant production systems across the region. All available documented evidence on the topic from 2000 to 2024 was collated and synthesised through standardised methods of systematic review protocol—PRISMA. Out of 319 research papers downloaded, six were included and analysed directly or indirectly in this study. The results show that the addition of feed additives to ruminant diets in all the studies reviewed significantly (p < 0.05) improved growth parameters such as average daily growth (ADG), feed intake, and feed conversion ratio (FCR) compared to the control group. However, no significant (p > 0.05) effect was found on cold carcass weight (CCW), meat percentage, fat percentage, bone percentage, or intramuscular fat (IMF%) compared to the control. The available evidence indicates that these additives can provide tangible benefits, including improved growth performance, better feed efficiency, enhanced immune responses, and superior meat quality, while also supporting environmental sustainability by reducing nitrogen excretion and decreasing dependence on antimicrobial agents. Full article

Climate change is one of the most important environmental problems of the modern world. Without an effective solution to this problem, it is not possible to implement sustainable development. For this reason, in the European development strategies, including the European Green Deal (EGD), the reduction in greenhouse gas (GHG) emissions is one of the priorities. This also applies to sectoral strategies, including those related to agriculture. In this context, the monitoring of changes in GHG emissions becomes particularly important, and its key condition is an applicative estimation method, adapted to the available data and levels of assessment (globally, country, sector, economic unit). GHG emission calculations at the level of the agricultural sector are officially estimated by the state and non-governmental organisations. However, calculations at the level of the agricultural unit-farm remain a challenge due to the lack of detailed data or its incomplete scope to estimate GHG emissions. The other issue is the necessity of a representative data nature, taking into consideration the different profiles of various farms. The research focused on presenting a methodological approach to utilising FADN (Farm Accountancy Data Network) data for estimating GHG emissions at the farm level. The Intergovernmental Panel on Climate Change (IPCC) methodology was adopted to use available farm-level data. Some assumptions were needed to achieve this goal. The article presents the subsequent stages of GHG calculation using the FADN data. The results reveal significant differences in GHG emissions among farm types. The presented results indicated the primary sources of emissions from agriculture, including energy (e.g., fuel and electricity consumption), thus outlining the scope of action that should be taken to reduce emissions effectively. The study confirms that the method used helps estimate emissions at the farm level. Its application can lead to better targeting of climate policy in agriculture. Full article

The Angat Reservoir serves as a major water source for Metro Manila, providing most of the region’s domestic, agricultural, and hydropower needs. However, its dependence on rainfall makes it sensitive to climate variability and future climate change. This study assesses potential long-term impacts of climate change on water availability in the Angat watershed using the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS). Historical rainfall data from 1994 to 2023 and projections under both RCP4.5 (moderate emissions) and RCP8.5 (high emissions) scenarios were analyzed to simulate future hydrologic responses. Results indicate projected reductions in wet-season rainfall and corresponding outflows, with declines of up to 18% under the high-emission scenario. Increased variability during dry-season flows suggests heightened risks of water scarcity. While these projections highlight possible changes in the watershed’s hydrologic regime, the study acknowledges limitations, including assumptions in rainfall downscaling and the absence of direct streamflow observations for model calibration. Overall, the findings underscore the need for further investigation and planning to manage potential climate-related impacts on water resources in Metro Manila. Full article

Aflatoxin (AF) contamination of crops during the pre-harvest period is a significant global concern for food and feed safety (FFS). In Europe, climate change presents a growing threat to agricultural products by increasing the risk of AF contamination. This umbrella review evaluates the scope and quality of pre-harvest data on climate-related AF contamination in Europe, addressing key questions: What insights do researchers provide on the relationship between climate change and pre-harvest AF contamination, and what data are lacking? Which crops are the focus of current research, and where in Europe are these studies concentrated? How is the data presented, and is it standardized? We conducted an umbrella literature review, extracting relevant studies from PubMed and Scopus up to 14 October 2024. Our findings indicate that rising temperatures, droughts, and shifting rainfall patterns increasingly favor the growth of aflatoxigenic fungi and pre-harvest AF contamination in European crops, posing risks to FFS and agricultural stability. However, inconsistencies in data collection and reporting limit cross-regional comparisons and hinder the development of effective mitigation strategies. Standardizing methodologies and improving data accessibility will enhance predictive modeling, strengthen risk assessments, and support targeted adaptation efforts, providing actionable insights for policymakers and agricultural stakeholders. Full article

Climate change presents major challenges to agriculture, especially in economically underdeveloped regions. In these areas, farmers often lack access to resources and timely information, which limits their ability to respond effectively to drought and threatens agricultural sustainability. This study uses survey data from farmers in underdeveloped regions of China to examine the association between their ability to acquire information and their drought response behaviors. The results indicate that better information acquisition ability is significantly correlated with more effective and scientifically informed decision-making in drought adaptation strategies. To explore the underlying mechanism, we introduce value perception—that is, farmers’ beliefs about the usefulness and benefits of drought adaptation strategies—as a mediating variable. A mechanism model is constructed to test how information acquisition ability relates to behavior indirectly through this perception. We apply a threshold regression model to identify potential nonlinear associations, finding that the relationship between information acquisition ability and drought response behaviors becomes stronger once a certain threshold is surpassed. Additionally, we employ the Item Response Theory (IRT) model to measure the intensity and quality of farmers’ adaptation behaviors more accurately. These findings provide theoretical insights and empirical evidence for enhancing agricultural resilience, while acknowledging that causality cannot be definitively established due to the cross-sectional nature of the data. The study also offers useful guidance for policymakers seeking to strengthen farmers’ access to information, improve value recognition of adaptive actions, and promote sustainable agricultural development in underdeveloped areas. Full article

As the global population continues to rise, the impact of urbanization on land utilization and ecosystems are growing more pronounced, particularly within the expanding area of Asia. The land use/land change (LULC) brought by urban expansion directly impacts plant growth and ecological productivity, altering the carbon cycle and climate regulation functions of the region. This research focuses on Harbin City as a case study, employing an enhanced version of the Carnegie–Ames–Stanford Approach (CASA) model to analyze the spatial–temporal variations in vegetation Net Primary Productivity (NPP) across the area from 2000 to 2020. The findings indicate that Net Primary Productivity (NPP) in Harbin exhibited notable interannual variability and spatial heterogeneity. From 2000 to 2005, a decline in NPP was observed across 60.75% of the area. This reduction was predominantly concentrated in the central and eastern areas of the city, where forested landscapes are the dominant feature. In contrast, from 2010 to 2015, 92.12% of the region saw an increase in NPP, closely related to the overall improvement in NPP across all land-use types. Land-use change significantly influenced NPP dynamics. Between 2000 and 2005, 54.26% of NPP increases stemmed from the transition of farmland into forest, highlighting the effectiveness of the “conversion of farmland back to forests” policy. From 2005 to 2010, 98.6% of the area experienced NPP decline, mainly due to forest and cropland degradation, especially the unstable carbon sink function of forest ecosystems. Between 2010 and 2015, NPP improved across 96.86% of the area, driven by forest productivity recovery and better agricultural management. These results demonstrate the profound and lasting impact of land-use transitions on the spatiotemporal dynamics of NPP. Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

Seasonal climate forecasts are an essential tool for providing early insight into weather-related impacts and supporting decision-making in sectors such as agriculture, energy, and disaster management. Accurate representation of atmospheric circulation at the seasonal scale is essential, especially in regions such as the Eastern Mediterranean, where complex synoptic patterns drive significant climate variability. The aim of this study is to perform a comparison of weather type classifications between ERA5 reanalysis and seasonal forecasts in order to assess the ability of seasonal data to capture the synoptic patterns over the Eastern Mediterranean. For this purpose, we introduce a regional seasonal forecasting framework based on the state-of-the-art Advanced Research WRF (WRF-ARW) model. A series of sensitivity experiments were also conducted to evaluate the robustness of the model’s performance under different configurations. Moreover, the ability of seasonal data to reproduce observed trends in weather types over the historical period is also examined. The classification results from both ERA5 and seasonal forecasts reveal a consistent dominance of anticyclonic weather types throughout most of the year, with a particularly strong signal during the summer months. Model evaluation indicates that seasonal forecasts achieve an accuracy of approximately 80% in predicting the daily synoptic condition (cyclonic or anticyclonic) up to three months in advance. These findings highlight the promising skill of seasonal datasets in capturing large-scale circulation features and their associated trends in the region. Full article

Biogas produced from agricultural and forestry waste is emerging as a strategic and multifunctional solution to address climate change, inefficient waste management, and the need for renewable energy by transforming large volumes of biomass. Global estimates indicate that approximately 1.3 billion tons of waste is produced each year for these sectors; this waste is processed through anaerobic digestion, allowing it to be transformed into energy and biofertilizers. This reduces greenhouse gas emissions by up to 90%, promotes rural development, improves biodiversity, and prevents environmental risks, such as forest fires. However, despite its high global technical potential, which is estimated at 8000 TWh per year, its use remains limited as a result of its high initial costs, low efficiency in relation to lignocellulosic waste, and weak regulatory frameworks, especially in countries like Mexico, which use less than 5% of their available biomass. In response, emerging technologies, such as co-digestion with microalgae, integrated biorefineries, and artificial intelligence tools, are opening up new avenues for overcoming these barriers under a comprehensive approach that combines science, technology, and community participation. Therefore, biogas is positioned as a key pillar for a circular, fair, and resilient bioeconomy, promoting energy security and advancing toward a just and environmentally responsible future. Full article

This study evaluates the impacts of Solar Radiation Management (SRM) on precipitation-related climate extremes in Southeast Asia. Using simulations from the Geoengineering Large Ensemble (GLENS), we assess spatial anomalies and differences in extreme precipitation indices—number of wet days (RR1), very heavy precipitation days (R20mm), maximum 5-day precipitation (Rx5day), consecutive dry days (CDD), and consecutive wet days (CWD)—relative to historical (1980–2009) and Representative Concentration Pathway 8.5 (RCP8.5) baselines. The results reveal that SRM induces highly heterogeneous precipitation responses across the region. While SRM increases rainfall frequency in parts of Indonesia, it reduces the number of wet days and lengthens dry spells over Vietnam, Thailand, and the Philippines. Spatial variations are also observed in changes to heavy precipitation days and multi-day rainfall events, with potential implications for flood and drought risks. These findings highlight the complex trade-offs in hydrological responses under SRM deployment, with important considerations for agriculture, water resource management, and climate adaptation strategies in Southeast Asia. Full article

The seasonal and interannual irregularity of temperature and precipitation is a feature of the Mediterranean climate that is intensified by climate change and constitutes a relevant driver of water and soil degradation. This study was developed during three years in a hydro-agricultural area of the Alqueva irrigation system (Portugal) with Mediterranean climate conditions. The sampling campaigns included collecting water samples from eight irrigation hydrants, analyzed four times yearly. The analysis incorporated meteorological data and indices (precipitation, temperature, and drought conditions) alongside chemical parameters, using multivariate statistics (factor analysis and cluster analysis) to identify key water quality drivers. Additionally, machine learning models (Random Forest regression and Gradient Boosting machine) were employed to predict electrical conductivity (ECw), sodium adsorption ratio (SAR), and pH based on chemical and climatic variables. Water quality evaluation showed a prevalence of a slight to moderate soil sodification risk. The factor analysis outcome was a three-factor model related to salinity, sodicity, and climate. The cluster analysis revealed a grouping pattern led by year and followed by stage, pointing to the influence of inter-annual climate irregularity. Variations in water quality from the reservoirs to the distribution network were not substantial. The Random Forest algorithm showed superior predictive accuracy, particularly for ECw and SAR, confirming its potential for the reliable forecasting of irrigation water quality. This research emphasizes the importance of integrating time-sensitive monitoring with data-driven predictions of water quality to support sustainable water resources management in agriculture. This integrated approach offers a promising framework for early warning and informed decision-making in the context of increasing drought vulnerability across Mediterranean agro-environments. Full article

Brazil is increasingly affected by extreme weather events due to climate change, with pronounced regional differences in temperature and precipitation patterns. The southeast region is particularly vulnerable, frequently experiencing severe droughts and extreme heatwaves linked to atmospheric blocking events and intense rainfall episodes driven by the South Atlantic Convergence Zone (SACZ). These phenomena contribute to recurring climate-related disasters. The country’s heavy reliance on hydropower heightens its susceptibility to droughts, while growing evidence points to intensifying dry spells and wildfires across multiple regions, threatening agricultural output and food security. Urban areas, particularly, are experiencing more frequent and severe heatwaves, posing serious health risks to vulnerable populations. This study investigates the links between global teleconnection indices and synoptic-scale systems, specifically blocking events and SACZ activity, and their influence on Brazil’s extreme heat, drought conditions, and river flow variability over the past 30 to 40 years. By clarifying these interactions, the research aims to enhance understanding of how large-scale atmospheric dynamics shape climate extremes and to assess their broader implications for water resource management, energy production, and regional climate variability. Full article

This study investigated the dynamic factors influencing food security in Saudi Arabia, a critical concern for the nation’s stability and development. The purpose of this research was to analyze the impact of several key determinants on the Food Security Index and to distinguish between their short-term and long-term effects, thereby providing evidence-based policy recommendations. Using annual time-series data spanning 1990 to 2023, the research employs the Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) methods. We specifically examined the roles of agricultural GDP contribution, agricultural insurance coverage, food price stability, government policies related to agriculture, climate change impacts, agricultural productivity, and technology adoption. Short-run estimates reveal that agricultural GDP contribution, government policies, and agricultural productivity express a significant positive influence on food security. Importantly, climate change showed a counterintuitive positive association in the short term, potentially indicating immediate adaptive responses. Conversely, food price stability exhibited an unexpected negative association, which may indicate that the index captures high price levels rather than just volatility. The long-run analysis highlights the crucial importance of sustained factors for food security. Agricultural GDP contribution, agricultural insurance coverage, and agricultural productivity are identified as having significant positive impacts over the long term. In contrast, climate change demonstrates a significant negative long-run impact, underscoring its detrimental effect over time. Government policies, while impactful in the short term, become statistically insignificant in the long run, suggesting that sustained structural factors become dominant. Granger causality tests indicate short-term causal relationships flowing from climate change (positively), agricultural GDP contribution, government policies, and agricultural productivity towards food security. The significant error correction term confirms the existence of a stable long-run equilibrium relationship among the variables. On the basis of these findings, the study concludes that strengthening food security in Saudi Arabia requires a multifaceted approach. Short-term efforts should focus on enhancing agricultural productivity and implementing targeted measures to mitigate immediate climate impacts and refine food price stabilization strategies. For long-term resilience, priorities must include expanding agricultural insurance coverage, investing in sustainable agricultural practices, and continuing to boost agricultural productivity. The study contributes to the literature by providing a comprehensive dynamic analysis of food security determinants in Saudi Arabia using robust time-series methods, offering specific insights into the varying influences of economic, policy, environmental, and agricultural factors across different time horizons. Further research is recommended to explore the specific mechanisms behind the observed short-term relationship with climate change and optimize food price policies. Full article

Understanding precipitation changes over a long period of time can provide valuable insights into global climate change. Taking the forest–steppe ecotone of North China as the research area, based on the tree ring width index of Carya cathayensis Sarg (Carya cathayensis), the relationship between tree growth and climate factors is analyzed, and the annual precipitation is reconstructed from data from the nearest five weather stations from AD 1540 to 2019. The results show that the growth of trees was affected by the changes in precipitation. The precipitation was divided into three dry periods and three wet periods over 480 years, based on wavelet analysis. There were 328 years of precipitation within the mean plus or minus one standard deviation (SD) (accounting for 68.3% of 480 years), indicating that relatively stable climate conditions exist in the study area, which has become one of the main agricultural areas in China. Each period lasted 2–7 years according to the multi-taper method, indicating that precipitation change was closely related to the El Niño–Southern Oscillation (ENSO) on a short time scale and affected by the Atlantic Multidecadal Oscillation (AMO) on a medium time scale during the period of 60–80 years based on wavelet analysis. Full article

Climate variability and extremes adversely affect the agricultural production system, food security, livestock sector, and water resources. With the cumulative effects of climate variability, there is a need to anticipate and develop appropriate adaptation strategies to cope with changing climatic conditions. It is necessary to study the adaptation strategies that are to be followed for climate change to examine the ability of vulnerable communities and people, frequently affected by drought and other climate-related risks, to adapt to climate change impacts. Hence, the present study examined the determinants of various climate change adaptation strategies followed by farmers as a measure to face climate variability, which will be ultimately beneficial and enlightening to policymakers to gain knowledge about the measures to be taken to mitigate the impact of climate change. The study was undertaken using data collected from 105 farm households with an organized pre-tested interview schedule in the central agro-climatic zone of Afghanistan. The multivariate probit econometric model was used to analyze the factors responsible for major adaptation strategies to mitigate the impact of climate change. The key findings of the model indicated that the probability of educated farmers migrating to the non-agricultural sector for employment has increased by 1.3 percent, and those who have more land area have adopted a reduction in irrigation by 5.2 percent as an adaptive mechanism. The study also found that having access to technical guidance from extension officials increased the likelihood of farmers changing their cropping pattern by 18.6 percent and of diversifying their farms by 19.2 percent. On the other hand, expert guidance reduced the likelihood of drilling new bore wells by 20.5 percentage points and decreased the probability of selling livestock by 10.8 percentage points. The results of the study provide policy insights to improve the ability of farmers to modify their practices through improvement in extension services, irrigation infrastructure facilities, watershed development, and climate-resilient agricultural systems. Full article