Climate change impact on major crop yield and water footprint under CMIP6 climate projections in repeated drought and flood areas in Thailand

Evaluation of farmland production potential in key agricultural production areas on the Qinghai-Tibet Plateau under multi-scenario simulation

Predicting changes in future land use and farmland production potential (FPP) within the context of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) is crucial for devising sustainable land use strategies that balance agricultural production and ecological conservation. Therefore, the Huangshui Basin (HSB) in the northeast Qinghai-Tibet Plateau is taken as the study area, and a LUCC-Plus-FPP (LPF) coupling framework based on the SSP-RCP scenarios is proposed to evaluate future land use patterns and FPP changes. On the basis of the predictions of land use changes from 2020 to 2070, the trade-offs in grain production resulting from bivariate changes in farmland and FPP under future scenarios are analyzed. The results indicate that the model has a high simulation accuracy for land use types, with an overall accuracy of 0.98, a kappa coefficient of 0.97, and a figure of merit value of 0.21. Under the SSP245 and SSP585 scenarios, built-up land increases significantly, by approximately 45.89 %. Farmland and grassland conversions contribute the most to increased built-up land. Farmland area consistently decreases by approximately 5 % across all scenarios. The protection of farmland in the study area is difficult to undertake and thus requires much attention. Moreover, under the SSP126 scenario, the FPP of most districts is greater than that in 2020, and the average FPP of the HSB from 2030 to 2070 is greater than that in 2020. In the SSP585 scenario, by 2070, the average FPP of all districts decreases to different degrees compared with that in 2020. Furthermore, the compensated farmland quantities and average FPPs under all the scenarios are significantly lower than the amount of occupied farmland. The results provide a theoretical foundation and data support for farmland protection decision-making and layout optimization in the Qinghai-Tibet Plateau.

Impacts of future climate change on rice yield based on crop model simulation-A meta-analysis

Rice is one of the world's major food crops. Changes in major climatic factors such as temperature, rainfall, solar radiation and carbon dioxide (CO2) concentration have an important impact on rice growth and yield. However, many of the current studies that predict the impact of future climate change on rice yield are affected by uncertainties such as climate models, climate scenarios, model parameters and structure, and showing great differences. This study was based on the assessment results of the impact of climate change on rice in the future of 111 published literature, and comprehensively analyzed the impact and uncertainty of climate change on rice yield. This study utilized local polynomial (Loess) regression analysis to investigate the impact of changes in mean temperature, minimum temperature, maximum temperature, solar radiation, and precipitation on relative rice yield variations within a complete dataset. A linear mixed-effects model was used to quantitatively analyze the relationships between the restricted datasets. The qualitative analysis based on the entire dataset revealed that rice yields decreased with increasing average temperature. The precipitation changed between 0 and 25 %, it was conducive to the stable production of rice, and when the precipitation changed >25 %, it would cause rice yield reduction. The change of solar radiation was less than -1.15 %, the rice yield increases with the increase of solar radiation, and when the change of solar radiation exceeds -1.15 %, the rice yield decreases. Elevated CO2 concentrations and management practices could mitigate the negative effects of climate change. The results of a quantitative analysis utilizing the mixed effects model revealed that average temperature, precipitation, CO2 concentration, and adaptation methods all had a substantial impact on rice production, and elevated CO2 concentrations and management practices could exert positive influences on rice production. For every 1 °C and 1 % increase in average temperature and precipitation, rice yield decreased by 3.85 % and 0.56 %, respectively. For every 100 ppm increase in CO2 concentration, rice yield increased by 7.1 %. The variation of rice yield under different climate models, study sites and climate scenarios had significant variability. Elevated CO2 concentrations and management practices could compensate for the negative effects of climate change, benefiting rice production. This study comprehensively collected and analyzed a wide range of literature and research, which provides an in-depth understanding of the impacts of climate change on rice production and informs future research and policy development.

Drought stress reduces arbuscular mycorrhizal colonization of Poncirus trifoliata (L.) roots and plant growth promotion via lipid metabolism

Drought stress poses increasingly serious threats to agricultural production in the era of global climate change. Arbuscular mycorrhizal (AM) fungi are well-recognized biostimulants promoting plant tolerance to drought stress. Lipids are indispensable for AM fungal colonization, however, the involvement of lipid metabolism in the drought tolerance conferred by AM fungi is largely unknown. In this study, we inoculated Poncirus trifoliata (L.) with Rhizophagus irregularis DAOM197198 under no drought stress, medium drought stress and severe drought stress, with non-inoculation under respective treatments as control. Results indicated that AM fungal inoculation significantly promoted the drought tolerance of P. trifoliata (L.), with the effect size decreasing along with drought severity. Moreover, the effect size was significantly related to arbuscule abundance. Fatty acid profiling showed that the arbuscule abundance was determined by the AM-specific phospholipids (PLs), whose biosynthesis and delivery were inhibited by drought stress as revealed by qRT-PCR of FatM, RAM1 and STR/STR2. More interestingly, AM fungal inoculation increased the lipid allocation to total PLs and the unsaturation rate of total neutral lipids (NLs), probably indicating the involvement of non-AM-specific lipids in the increased drought tolerance. Taken together, our results demonstrate that lipid metabolism in AM mediates the increased drought tolerance conferred by AM fungal inoculation, with AM-specific and non-AM-specific lipids functioning therein in different ways.

Climate change reduces elevational and latitudinal differences in spring phenology of pine caterpillar (Dendrolimus spectabilis Bulter)

The pine caterpillar (Dendrolimus spectabilis Bulter, Lepidoptera: Lasiocampidae), as an ectotherm, temperature plays a crucial role in its development. With climate change, earlier development of insect pests is expected to pose a more frequent threat to forest communities. Yet the quantitative research about the extent to which global warming affects pine caterpillar populations is rarely understood, particularly across various elevations and latitudes. Spring phenology of pine caterpillars showed an advancing trend with 0.8 d/10a, 2.2 d/10a, 2.2 d/10a, and 3.3 d/10a under the SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenario, respectively. There was a maximum advance of 20 d in spring phenology of pine caterpillars during the 2090s, from mid-March to early March, and even late February. This study highlighted the significant advance in spring phenology at elevations >1000 m and lower latitudes. Consequently, the differences in elevational and latitudinal gradients were relatively small as the increasing temperatures at the end of the 21st century. And the average temperature in February-March was effective in explaining theses variability. These findings are crucial for adapting and mitigating to climate change.

The Global Environmental Benefits of Halving Avoidable Consumer Food Waste

Avoidable consumer food waste (ACFW) is a global environmental issue wasting key resources and causing emissions, especially in high food-producing nations. We trace ACFW to its origin to assess emissions, water use, and land use. We show that ACFW impacts are dominated by commodities like beef, dairy, rice, and wheat. Over 80% of impacts are domestic, but impacts embodied in trade affect a few major food-producing countries under environmental pressure. A 50% reduction in ACFW could save up to 198 Mt CO2eq in emissions, 30 Gm3 of blue water, and 99 Mha of land. Targeting key commodities in impactful countries (e.g., US beef waste) could achieve significant benefits. Sparing wasted land and returning it to its potential natural vegetation could sequester 26 Gt CO2eq long-term (17-35 Gt CO2eq). Finally, while the 50% ACFW reduction lines up with Sustainable Development Goal (SDG) 12.3b for the avoidable portion of food waste, a total of 276 Mt of unavoidable consumer food waste is also generated, which cannot be readily reduced. Achieving a 50% reduction in total food waste would require a 93% reduction in ACFW. Tracking the spatial impacts of ACFW can elucidate the concrete benefits of policies aiming at SDG 12.3b.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Interaction of drought-influencing factors for drought mitigation strategies in Lam Ta Kong Watershed, Khorat Plateau

Generally, drought is influenced by both spatial characteristics and anthropogenic activities within an area. Drought vulnerability assessment is a critical tool that can be effectively used to develop proper drought mitigation strategies to prevent avoidable losses. To develop suitable drought mitigation strategies, the overall drought vulnerability must be assessed, and the interaction among drought-influencing factors in the area should be considered. Consequently, this study aimed to investigate the interactions among critical drought-influencing factors and drought vulnerability in the Lam Ta Kong Watershed via spatial analysis with the analytical hierarchy process (AHP) and geographical information system (GIS) technology. Ten drought-influencing factors were considered in the vulnerability assessment: slope, elevation, soil texture, soil fertility, stream density, precipitation, temperature, precipitation days, evaporation, and land use. The results indicated that the critical drought-influencing factors were precipitation, precipitation days, and land use, resulting in most of the watershed experiencing high drought vulnerability (35.1% of the watershed or 1810.83 km2). Moreover, this research highlighted the interactions among the critical drought-influencing factors. Precipitation interacted with precipitation days to cause drought vulnerability across the watershed, with a p-value <0.05. Similarly, the interactions between precipitation and land use and between precipitation days and land use, with p-values <0.05, showed that they were associated with and influenced by drought in the Lam Ta Kong Watershed. This study further indicated that appropriate drought mitigation strategies for this watershed must consider the interactions among these drought-influencing factors, as well as their specific interactions across the watershed.

Future climate change impacts on wheat grain yield and protein in the North China Region

The threat of global climate change on wheat production may be underestimated by the limited capacity of many crop models to predict grain quality and protein composition. This study aimed to integrate a wheat quality module of protein components into the CROPSIM-CERES-Wheat model to investigate the impact of climate change on wheat grain yield and protein quality in the North China Region (NCR) using five Global Climate Models (GCMs) from CMIP6 under three shared socioeconomic pathways. The CERES-Wheat model with a quality module was developed and calibrated and validated using data from several sites in the NCR. The results of the calibration and validation showed that the modified CERES-Wheat model can accurately predict grain yield, protein content and its components in field experiments. Compared with the baseline period (1981-2010), the annual mean temperature and annual cumulative precipitation increased in the NCR in the 2030's, 2050's and 2080's. The radiation was higher under the SSP126 and SSP585 scenarios, and lower under the SSP370 scenario compared to the baseline period. The anthesis and maturity date occurred earlier under the three future scenarios. The average grain yield increased by 13.3-30.9 % under three future scenarios. However, the regional average grain protein content of winter wheat in the future decreased by 2.0 %- 3.5 %. The reduction in wheat grain protein at the regional was less pronounced under SSP370 than that under SSP126 and SSP585. The structural protein content of winter wheat decreased under future climate conditions compared with the baseline period, but the storage protein content showed the opposite tendency. The model provided a useful tool to study the effects of future climate on grain quality and protein composition. These findings are important for developing agricultural practices and strategies to mitigate the potential impacts of climate change on wheat production and wheat quality in the future.

Response of Wheat, Maize, and Rice to Changes in Temperature, Precipitation, CO2 Concentration, and Uncertainty Based on Crop Simulation Approaches

The influence of global climate change on agricultural productivity is an essential issue of ongoing concern. The growth and development of wheat, maize, and rice are influenced by elevated atmospheric CO₂ concentrations, increased temperatures, and seasonal rainfall patterns. However, due to differences in research methodologies (e.g., crop models, climate models, and climate scenarios), there is uncertainty in the existing studies regarding the magnitude and direction of future climate change impacts on crop yields. In order to completely assess the possible consequences of climate change and adaptation measures on crop production and to analyze the associated uncertainties, a database of future crop yield changes was developed using 68 published studies (including 1842 samples). A local polynomial approach was used with the full dataset to investigate the response of crop yield changes to variations in maximum and minimum temperatures, mean temperature, precipitation, and CO₂ concentrations. Then, a linear mixed-effects regression model was utilized with the limited dataset to explore the quantitative relationships between them. It was found that maximum temperature, precipitation, adaptation measure, study area, and climate model had significant effects on changes in crop yield. Crop yield will decline by 4.21% for each 1 °C rise in maximum temperature and increase by 0.43% for each 1% rise in precipitation. While higher CO₂ concentrations and suitable management strategies could mitigate the negative effects of warming temperatures, crop yield with adaptation measures increased by 64.09% compared to crop yield without adaptation measures. Moreover, the uncertainty of simulations can be decreased by using numerous climate models. The results may be utilized to guide policy regarding the influence of climate change and to promote the creation of adaptation plans that will increase crop systems' resilience in the future.

Assessing Future Climate Change Impacts on Potato Yields — A Case Study for Prince Edward Island, Canada

Crop yields are adversely affected by climate change; therefore, it is crucial to develop climate adaptation strategies to mitigate the impacts of increasing climate variability on the agriculture system to ensure food security. As one of the largest potato-producing provinces in Canada, Prince Edward Island (PEI) has recently experienced significant instability in potato production. PEI’s local farmers and stakeholders are extremely concerned about the prospects for the future of potato farming industries in the context of climate change. This study aims to use the Decision Support System for Agrotechnology Transfer (DSSAT) potato model to simulate future potato yields under the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios (including SSP1–1.9, SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5). The study evaluates the combined effects of changing climatic conditions at local scales (i.e., warming temperature and changing precipitation patterns) and increasing carbon dioxide (CO2) concentration in the atmosphere. The results indicate future significant declines in potato yield in PEI under the current farming practices. In particular, under the high-emission scenarios (e.g., SSP3–7.0 and SSP5–8.5), the potato yield in PEI would decline by 48% and 60% in the 2070s and by 63% and 80% by 2090s; even under the low-emission scenarios (i.e., SSP1–1.9 and SSP1–2.6), the potato yield in PEI would still decline by 6–10%. This implies that it is important to develop effective climate adaptation measures (e.g., adjusting farming practices and introducing supplemental irrigation plans) to ensure the long-term sustainability of potato production in PEI.

Tamarixia radiata global distribution to current and future climate using the climate change experiment (CLIMEX) model

The phloem-limited bacteria, "Candidatus Liberibacter asiaticus" and "Ca. L. americanus", are the causal pathogens responsible for Huanglongbing (HLB). The Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) is the principal vector of these "Ca. Liberibacter" species. Though Tamarixia radiata Waterston (Hymenoptera: Eulophidae) has been useful in biological control programmes against D. citri, information on its global distribution remains vague. Using the Climate Change Experiment (CLIMEX) model, the potential global distribution of T. radiata under the 2050s, 2070s, and 2090s for Special Report on Emissions Scenarios A1B and A2 was defined globally. The results showed that habitat suitability for T. radiata covered Africa, Asia, Europe, Oceania, and the Americas. The model predicted climate suitable areas for T. radiata beyond its presently known native and non-native areas. The new locations predicted to have habitat suitability for T. radiata included parts of Europe and Oceania. Under the different climate change scenarios, the model predicted contraction of high habitat suitability (EI > 30) for T. radiata from the 2050s to the 2090s. Nevertheless, the distribution maps created using the CLIMEX model may be helpful in the search for and release of T. radiata in new regions.

Hydrological Drought Severity in Different Return Periods in Rivers of Ardabil Province, Iran

Hydrological drought (HD) characterization with different return periods is essential to appropriately design the best water management practices. In particular, characterizing the interactive relations of discharge, drought, and return periods using a novel triple diagram can deepen the interpretation of regional droughts, which have not been adequately considered, especially in semi-arid areas. Considering the critical role of HD in water exploitation and management in Iran, this study was therefore conducted to analyze the HD in different return periods in rivers of the Ardabil Province (area = 17,953 km2). To this end, the streamflow drought index (SDI) was computed using DrinC software at 1-, 3-, and 6-month time scales for 25 hydrometric stations during 1981–2014. Then, the drought severity was evaluated by CumFreq software in different return periods (2, 5, 10, 25, 50, and 100 years). Finally, the relationship between discharge, SDI, and return periods was analyzed using triple diagram models. The results revealed that the drought events had mild (−1 ≤ SDI < 0) and moderate (−1.5 ≤ SDI < −1) severity for most study stations in the study area. The mean values of SDI in the 1-, 3-, and 6-month time scales were 1.08, 0.80, and 0.55, respectively. At all study time scales, the drought severity in both rivers with low and high flows increased with increasing return periods. In such a way, the maximum drought severity has been found for rivers with high flow at a 100-year return period. The current results can be considered a screening tool for the distinctive conservation and directive management of watershed resources.

Assessing impacts of global climate change on water and food security in the black soil region of Northeast China using an improved SWAT-CO2 model

Future climate change may have substantial impacts on both water resources and food security in China's black soil region. The Liao River Basin (LRB; 220,000 km²) is representative of the main black soil area, making it ideal for studying climate change effects on black soil. In this study, the Soil and Water Assessment Tool (SWAT) model was first initialized for the LRB. Actual evapotranspiration (ET_a) values calculated using the Surface Energy Balance System (SEBS) model and city-level corn (Zea mays L.) yields were then used to calibrate the SWAT model. Finally, the SWAT model was modified to accept dynamic CO₂ input and output crop transpiration, soil evaporation, and canopy interception separately to explore the impacts of future climate change on ET related variables and crop water productivity (CWP) in the LRB. Simulation scenario design included 22 General Circulation Models (GCMs) and 4 Shared Socioeconomic Pathways (SSPs) scenarios from the latest Coupled Model Intercomparison Project 6 (CMIP6) for two 30-year periods of 2041-2070 and 2071-2100. The predicted results showed a significant (P < 0.05) increase in air temperatures and precipitation in the LRB. In contrast, solar radiation decreased significantly and was most reduced for the SSP3-7.0 scenario. Reference evapotranspiration (ET_o), ET_a, and soil evaporation significantly increased in future scenarios, while canopy interception and crop transpiration showed significant reductions, particularly under the 2071-2100 SSP5-8.5 scenario. Overall, corn yield elevated considerably (P < 0.05) with the largest increase for the SSP5-8.5 scenario during 2071-2100. However, the SSP3-7.0 scenario indicated a significant decline in yield. Future changes in CWP were similar to those for corn yield, with significant increases in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. These findings suggested future climate change may have a positive impact on corn production in the black soil region of the LRB.

Future global concurrent droughts and their effects on maize yield

Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950-2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950-2014), near future (2021-2060), and far future (2061-2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions.

Impacts of Drought and Rehydration Cycles on Isoprene Emissions in Populus nigra Seedlings

The volatile organic compounds emitted by plants significantly impact the atmospheric environment. The impacts of drought stress on the biogenic volatile organic compound (BVOC) emissions of plants are still under debate. In this study, the effects of two drought-rehydration cycle groups with different durations on isoprene emissions from Populus nigra (black poplar) seedlings were studied. The P. nigra seedlings were placed in a chamber that controlled the soil water content, radiation, and temperature. The daily emissions of isoprene and physiological parameters were measured. The emission rates of isoprene (F_iso) reached the maximum on the third day (D3), increasing by 58.0% and 64.2% compared with the controlled groups, respectively, and then F_iso significantly decreased. Photosynthesis decreased by 34.2% and 21.6% in D3 in the first and second groups, respectively. After rehydration, F_iso and photosynthesis recovered fully in two groups. However, F_iso showed distinct inconsistencies in two groups, and the recovery rates of F_iso in the second drought group were slower than the recovery rates of F_iso in the first groups. The response of BVOC emissions during the drought-rehydration cycle was classified into three phases, including stimulated, inhibited, and restored after rehydration. The emission pattern of isoprene indicated that isoprene played an important role in the response of plants to drought stress. A drought-rehydration model was constructed, which indicated the regularity of BVOC emissions in the drought-rehydration cycle. BVOC emissions were extremely sensitive to drought, especially during droughts of short duration. Parameters in computational models related to BVOC emissions of plants under drought stress should be continuously improved.

Water footprint and virtual water trade analysis in water-rich basins: Case of the Chaohu Lake Basin in China

Water footprints and virtual water are widely used as essential tools for water use and conservation analysis of basins worldwide. Despite the importance of water-rich basins as the main force for water-saving, water use analysis has been mainly for water-scarce basins rather than water-rich basins in the existing literature. To fill the gap, in this paper, we investigate the water footprint and virtual water trade in a water-rich basin, namely the Chaohu Lake Basin in China, from 2007 to 2017 using input-output analysis. The results show that: (1) Water use efficiency in the Chaohu Lake Basin was significantly improved. The overall trend of the water intensity was declining, decreasing by 10.21 % in 2017 versus 2012; (2) The internal and external water footprints showed an upward trend, and the growth rate of total water footprint was 36.66 %; (3) The basin was a net virtual water exporter, but the net export flows of virtual water has decreased significantly. The virtual water net export flow decreased by 0.12 billion m³ in 2017 versus 2012; (4) Water resources in the basin were mainly used locally, and its supply to other provinces was minimal. Compared with some water-scarce basins such as the Heihe River Basin and Haihe River Basin, the Chaohu Lake Basin shows significant gaps in the virtual water export flow per capita and behaves differently in the proportion of virtual water transfer. Based on the above findings, we conclude with some guidance and implications for local governments and policymakers.

In Silico Approach of Glycinin and Conglycinin Chains of Soybean By-Product (Okara) Using Papain and Bromelain

This study explores utilization of a sustainable soybean by-product (okara) based on in silico approach. In silico approaches, as well as the BIOPEP database, PeptideRanker database, Peptide Calculator database (Pepcalc), ToxinPred database, and AllerTop database, were employed to evaluate the potential of glycinin and conglycinin derived peptides as a potential source of bioactive peptides. These major protein precursors have been found as protein in okara as a soybean by-product. Furthermore, primary structure, biological potential, and physicochemical, sensory, and allergenic characteristics of the theoretically released antioxidant peptides were predicted in this research. Glycinin and α subunits of β-conglycinin were selected as potential precursors of bioactive peptides based on in silico analysis. The most notable among these are antioxidant peptides. First, the potential of protein precursors for releasing bioactive peptides was evaluated by determining the frequency of occurrence of fragments with a given activity. Through the BIOPEP database analysis, there are several antioxidant bioactive peptides in glycinin and β and α subunits of β-conglycinin sequences. Then, an in silico proteolysis using selected enzymes (papain, bromelain) to obtain antioxidant peptides was investigated and then analyzed using PeptideRanker and Pepcalc. Allergenic analysis using the AllerTop revealed that all in silico proteolysis-derived antioxidant peptides are probably nonallergenic peptides. We also performed molecular docking against MPO (myeloperoxidases) for this peptide. Overall, the present study highlights that glycinin and β and α subunits of β-conglycinin could be promising precursors of bioactive peptides that have an antioxidant peptide for developing several applications.

Potential impact of diversion canals and retention areas as climate change adaptation measures on flood risk reduction: A hydrological modelling case study from the Chao Phraya River Basin, Thailand

The countries of Southeast Asia are projected to experience severe flood damage and economic impacts from climate change, compared with the global average. Hence adaptation by incorporating infrastructures is essential, but it has been seldom explicitly included in the simulations projecting climate change impacts on flood risk in these countries. Quantifying the effects of infrastructure is the key to climate change impact and adaptation assessment. Therefore, this study was conducted in the Chao Phraya River Basin (CPRB) in Thailand to examine the adaptation potential of (i) existing structural and non-structural measures that include reservoir and diversion dams, diversion canals, and water retention areas, and (ii) the combined adaptation measures, a combination of alterations made to the existing diversion canals and retention areas, on reducing future floods using the H08 global hydrological model (GHM). The results revealed that the impact of existing measures on the future flood reduction was smaller than the increase caused by warming in the CPRB. Conversely, the combined adaptation measures successfully mitigated the effect of warming by redirecting nearly 50 % of the diverted river flow to the ocean and storing 30 % of the diverted flow in the retention areas. Although a remarkable reduction was noted in the basin-wide flood risk, the effect of adaptation measures greatly varied across the basin. The combined adaptation measures largely reduced the number of flooding days by close to 100 at many of the considered stations within the basin, except for extreme flood events (historical 1-percentile flood events). This further reveals that the feasibility of adaptation measures in alleviating the extreme future floods will be limited in flood-vulnerable basins and thus require area-based prioritization for flood management. The modelling framework implemented in this study can be easily adapted to different GHMs and regions and should be examined for their applicability.

Towards a sustainable food production: modelling the impacts of climate change on maize and soybean production in Ghana

The Ghanaian economy relies heavily on maize and soybean production. The entire maize and soybean production system is low-tech, making it extremely susceptible to environmental factors. As a result, climate change and variability have an influence on agricultural production, such as maize and soybean yields. Therefore, the study's ultimate purpose was to analyze the influence of CO2 emissions, precipitation, domestic credit, and fertilizer consumption on maize and soybean productivity in Ghana by utilizing the newly constructed dynamic simulated autoregressive distributed lag (ARDL) model for the period 1990 to 2020. The findings indicated that climate change enhances maize and soybean yields in Ghana in both the short run and long run. Also, the results from the frequency domain causality showed that climate change causes maize and soybean yield in the long-run. These outcomes were robust to the use of the ordinary least squares estimator and the impulse response technique. The findings show that crop and water management strategies, as well as information availability, should be considered in food production to improve resistance to climate change and adverse climatic circumstances.

Virtual Water Flow Pattern in the Yellow River Basin, China: An Analysis Based on a Multiregional Input-Output Model

Research on the Yellow River Basin's virtual water is not only beneficial for rational water resource regulation and allocation, but it is also a crucial means of relieving the pressures of a shortage of water resources. The water stress index and pull coefficient have been introduced to calculate the implied virtual water from intraregional and interregional trade in the Yellow River Basin on the basis of a multi-regional input-output model; a systematic study of virtual water flow has been conducted. The analysis illustrated that: (1) Agriculture is the leading sector in terms of virtual water input and output among all provinces in the Yellow River Basin, which explains the high usage. Therefore, it is important to note that the agricultural sector needs to improve its water efficiency. In addition to agriculture, virtual water is mainly exported through supply companies in the upper reaches; the middle reaches mainly output services and the transportation industry, and the lower reaches mainly output to the manufacturing industry. Significant differences exist in the pull coefficients of the same sectors in different provinces (regions). The average pull coefficients of the manufacturing, mining, and construction industries are large, so it is necessary to formulate stricter water use policies. (2) The whole basin is in a state of virtual net water input, that is, throughout the region. The Henan, Shandong, Shanxi, Shaanxi, and Qinghai Provinces, which are relatively short of water, import virtual water to relieve local water pressures. However, in the Gansu Province and the Ningxia Autonomous Region, where water resources are not abundant, continuous virtual water output will exacerbate the local resource shortage. (3) The Yellow River Basin's virtual water resources have obvious geographical distribution characteristics. The cross-provincial trade volume in the downstream area is high; the virtual water trade volume in the upstream area is low, as it is in the midstream and downstream areas; the trade relationship is insufficient. The Henan and Shandong Provinces are located in the dominant flow direction of Yellow River Basin's virtual water, while Gansu and Inner Mongolia are at the major water sources. Trade exchanges between the midstream and downstream and the upstream should be strengthened. Therefore, the utilization of water resources should be planned nationwide to reduce water pressures, and policymakers should improve the performance of agricultural water use within the Yellow River Basin and change the main trade industries according to the resource advantages and water resources situation of each of them.

Assessment of Sectoral Virtual Water Flows and Future Water Requirement in Agriculture Under SSP-RCP Scenarios: Reflections for Water Resources Management in Zhangye City

Water scarcity is a core issue that constraints the high-quality development of arid areas in northwestern China. Zhangye is an oasis city located in the Heihe River Basin in northwestern China. It is populated with an agriculture-dominated economy and faces more and more serious water crises. Virtual water is an indicator that can measure the embodied water in the traded products, which has been widely applied for making rational policies for water resources management. In addition, clarifying water requirements in agricultural sectors under future climate change scenarios is essential to develop more appropriate adaptation strategies. From this perspective, this study aims to evaluate and compare virtual water flows among various sectors in Zhangye for the years 2012 and 2017 with a single regional input-output model and to further clarify the future water requirement tendency in agriculture during 2020–2050 under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) scenarios. The results showed that the planting sector directly contributed most of the total water consumption with the highest direct coefficient of 3307.5 m3/yuan in 2012, whereas the manufacture of food products and tobacco processing sector had the largest proportion of indirect water consumption (99%) mainly from intermediate inputs of agricultural products. Water consumption intensity of all sectors on average decreased by 22% during 2012–2017, indicating an increasing water utilization efficiency in economic industries. Household consumption also can improve water utilization efficiency as the major pathway for final consumption (86.4% in 2017). Water scarcity in Zhangye was becoming increasingly prominent since virtual water net exports were higher than local consumption, especially in the agriculture, manufacturing, and energy supply industries. Moreover, under climate change scenarios, we found the highest level of water requirement per unit area occurred in 2000, but it still had an incremental potential by 2050, especially in SSP585. The high requirement intensity and large-scale maize planting caused a rising tendency of total crop water requirement with an annual increasing rate of 8.4% from 1980 to 2050. This makes it possible to adapt to climate change through scientific management measures and technical means. We further made policy implications for adaptive management of water resources in Zhangye.

A bibliometric review on the inter-connection between climate change and rice farming

Rice is an important cereal and a staple food in many countries in the world. Climate change is a significant challenge that affects paddy production and threatens food security. However, research and development in this area continue to work to ensure the supply of rice fulfils the demands of the population. The study aims to analyse the transformation of international research power in trends in climate change that threaten food security (rice) worldwide. This study evaluates existing publications, especially research works from the period 1970 to 2020. The Web of Science database and the VOSviewer software were used together to generate a systematic analysis. A total of 1181 publications on climate change and paddy production were identified, written by 2249 authors from 56 countries. The highest number of publications was from China with 240 publications with 4609 citations, followed by India, with 225 publications and 2070 citations. Yield and adaptation are the most frequently used keywords that reflect this field's current significant research direction. Besides that, developing countries have received greater attention from researchers to focus on science, agriculture, climatology, and agriculture engineering as their domains. Therefore, socio-economic aspects should also be highlighted to raise awareness of the dangers of climate change and improve the farmers' economy by increasing paddy production. Attention was given by all countries globally, especially by researchers and stakeholders who need to plan holistic policies and strategies to encourage sustainable rice production and at the same time reduce the impact of climate change worldwide.

Evaluation and Projection of Diurnal Temperature Range in Maize Cultivation Areas in China Based on CMIP6 Models

The diurnal temperature range (DTR) is an important meteorological component affecting maize yield. The accuracy of climate models simulating DTR directly affects the projection of maize production. We evaluate the ability of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models to simulate DTR during 1961–2014 in maize cultivation areas with the observation (CN05.1), and project DTR under different shared socioeconomic pathway (SSP) scenarios. The root mean square error (RMSE), standard deviation (SD), Kling-Gupta efficiency (KGE) and comprehensive rating index (CRI) are used in the evaluation of the optimal model. The results show that CMIP6 models can generally reproduce the spatial distribution. The reproducibility of the annual average DTR in the maize cultivation areas is better than that in China but lower for the maize-growing season. The optimal model (EC-Earth3-Veg-LR) is used in the projection. Under the two SSPs, the DTR decreases compared with the historical period, especially in Northwest and North China. The DTR under SSP245 remains unchanged (annual) or increases slightly (growing season) during 2015–2050, while a significant decreasing trend is observed under SSP585. This highlights the importance of evaluating DTR in maize cultivation areas, which is helpful to further improve the accuracy of maize yield prediction.