Effect of Climate Change on Maize Yield in the Growing Season: A Case Study of the Songliao Plain Maize Belt

Assessing the effects of drought on rainfed maize water footprints based on remote sensing approaches

BACKGROUND

Drought affects the characteristics of water use during crop production and so quantitatively evaluating the impacts is important. However, it remains unclear how crop water use responds to drought. To address this gap, water footprint (WF) and standardized precipitation evapotranspiration index (SPEI) were calculated by remote sensing approaches to assess the effects of drought on crop water use. Rainfed maize is the most important crop in Jilin Province, and its growth and water use are more susceptible to drought. The present study explored not only the impact of growing season drought on the maize WF values in Jilin Province, but also the response of WF values to drought at different time scales.

RESULTS

Spatially, 72.94% of the WFblue pixels showed a non-significant increase, and the WFgreen in 68% pixels decreased significantly, being mainly concentrated in the middle region. Furthermore, the pixels affected by monthly time scale drought were mainly in the middle region, whereas the pixels affected by annual time scale drought were mainly in the western region.

CONCLUSION

Drought not only affected on the source and structure of agricultural water consumption, but also had different effects on WF values at different time scale. These effects had obvious spatial differences. The present study systematically explored the effects of drought on the WF values for rainfed maize in different climate regions and a consideration of these effects could provide valuable information on rainfed maize growth and the agricultural water use response to a changing climate. © 2023 Society of Chemical Industry.

Maize Production under Drought Stress: Nutrient Supply, Yield Prediction

Maize yield forecasting is important for the organisation of harvesting and storage, for the estimation of the commodity base and for the provision of the country's feed and food demand (export-import). To this end, a field experiment was conducted in dry (2021) and extreme dry (2022) years to track the development of the crop to determine the evolution of the relative chlorophyll content (SPAD) and leaf area index (LAI) for better yield estimation. The obtained results showed that SPAD and LAI decreased significantly under drought stress, and leaf senescence had already started in the early vegetative stage. The amount of top dressing applied at V6 and V12 phenophases did not increase yield due to the low amount of rainfall. The 120 kg N ha-1 base fertiliser proved to be optimal. The suitability of SPAD and LAI for maize yield estimation was modelled by regression analysis. Results showed that the combined SPAD-LAI was suitable for yield prediction, and the correlation was strongest at the VT stage (R2 = 0.762).

Dependence of maize yield on hydrothermal factors in various agro-climatic zones of the Rostov region of Russia in the context of climate change

Trends in mean monthly temperature and precipitation during the growing season and their effects on the maize yield were analyzed at the Zimovnikovsky (Zim) and Rostov (Ros) state variety plots (SVPs), located in different agro-climatic zones of the Rostov region. For these two SVPs, in the period of 1975-2019, the Mann-Kendall test showed a statistically significant increase (p < 0.05) in mean temperature (0.70 and 0.52 °C/decade) and a trend of decreased total precipitation (- 14.81 and - 10.40 mm/decade) during the maize growing season. The dependence of the maize yield on hydrothermal factors was estimated for the period of 2011-2019 using the Pearson correlation coefficient (p < 0.05). The mean temperature in September at Zim negatively (r =  - 0.78), and in June at Ros positively (r = 0.77) correlated with yield, which explained, according to the value of the coefficient of determination (R²), up to 60.7% and 58.7%, respectively, of the interannual variability of the maize yield. The precipitation in July at the Zim and Ros positively correlated (r = 0.75 and r = 0.71) with yield and explained up to 55.9% and 50.6%, respectively, of the interannual variability of the maize yield. The total amount of precipitation during the growing season at Zim was the dominant factor, explaining up to 75.7% of the interannual variability of maize yield. The continuation of the observed climatic trends during the growing season could lead in the next decade to both a decrease in the maize yield by an average of 0.25 t/ha at Zim and an increase in the maize yield by an average of 0.42 t/ha at Ros.

Assessment of Maize Drought Risk in Midwestern Jilin Province: A Comparative Analysis of TOPSIS and VIKOR Models

Drought is a major natural disaster that causes a reduction in rain-fed maize yield. Agricultural drought risk assessment is conducive to improving regional disaster management ability, thereby reducing food security risks and economic losses. Considering the complexity of risk assessment research, an increasing number of researchers are focusing on the multiple-criteria decision-making (MCDM) method. However, the applicability of the MCDM method to agro-meteorological disaster risk assessments is not clear. Therefore, this study comprehensively evaluated hazard, exposure, vulnerability, and emergency response and recovery capability using the TOPSIS and VIKOR models to generate a maize drought risk map in mid-western Jilin Province and ranked the drought risk of each county. The results showed that: (1) maize drought risk in the middle and west of Jilin province showed an increasing trend. Spatially, the risk diminished from west to east. The drought risks faced by Tongyu, Changchun, and Dehui were more severe; (2) the evaluation results of the two models were verified using the yield reduction rate. The VIKOR model was found to be more suitable for agrometeorological drought risk assessments; (3) according to the damage degree of drought disaster to maize, the cluster analysis method was used to divide the study area into three sub-regions: safe, moderate drought, and severe drought. Combined with the characteristics of different regions, suggestions on disaster prevention and mitigation are proposed. The results of this study can provide a basis for formulating strategies to alleviate drought, reduce losses, and ensure the sustainable development of agriculture.

Comprehensive Risk Assessment of High Temperature Disaster to Kiwifruit in Shaanxi Province, China

In recent years, the main kiwifruit producing region, central-south Shaanxi Province, has often suffered from the threat of extreme high temperatures. Assessing the risk of high-temperature disasters in the region is essential for the rational planning of agricultural production and the development of resilience measures. In this study, a database was established to assess the risk of a high-temperature disaster to kiwifruit. Then, four aspects, hazard, vulnerability, exposure and disaster prevention and mitigation capacity, were taken into account and 19 indexes were selected to make an assessment of the risk of a high-temperature disaster. At the same time, 16 indexes were selected for the assessment of the climatic suitability of kiwifruit in terms of light, heat, water, soil and topography, and were used as one of the indexes for exposure assessment. The analytic hierarchy process and the entropy weighting method were combined to solve the weights for each index. The results reveal that: (1) The Guanzhong Plain has a high climatic suitability for kiwifruit, accounting for 15.14% of the study area. (2) The central part of the study area and southern Shaanxi are at high risk, accounting for 22.7% of the study area. The major kiwifruit producing areas in Shaanxi Province (e.g., Baoji) are at a low risk level, which is conducive to the development of the kiwifruit industry. Our study is the first to provide a comprehensive assessment of the risk of a high-temperature disaster to the economic fruit kiwifruit, providing a reference for disaster resilience and mitigation.

Spatiotemporal Variation of Water Supply and Demand Balance under Drought Risk and Its Relationship with Maize Yield: A Case Study in Midwestern Jilin Province, China

Under the background of global warming, the frequent occurrence and long-term persistence of drought events have substantial negative effects on agricultural production. As the main maize production area in midwestern Jilin Province, frequent drought and a shortage of irrigation water pose substantial threats to the production of maize. We analyzed the balance of water supply and demand in each growth period and the degree of maize yield affected by drought. The results indicate that the FIO-ESM climate model can effectively simulate the changes in temperature and precipitation, and was highly applicable to the study area. From 1980 to 2020, the drought risk indices for the sowing to jointing, jointing to tasseling, tasseling to milk-ripe, and milk-ripe to maturity stages were 0.62, 0.52, 0.48, and 0.60, respectively. In the future, the chances of a RCP8.5 scenario drought risk and an enhanced RCP4.5 scenario have eased. Spatially, the high-risk areas shift in a “west−central−southwest” pattern. Effective precipitation will decrease in the future, while the increasing water requirement of maize increases the dependence on irrigation water. The irrigation requirement index is more than 70% for all periods, particularly in the milk-ripe to maturity stage. The relative meteorological yields were positively correlated with the CWDI of the whole growth period, with the rate of reduction in maize yield and the yield reduction coefficient of variation at a high level of risk between 1980 and 2020. In the future, the negative impact of drought risk on the yield of maize lessened with no obvious trend in production. In particular, the rate of reduction and reduction coefficient of variation for the RCP8.5 scenario were 1.24 and 1.09, respectively.

Drought Risk Analysis, Forecasting and Assessment under Climate Change

Climate change is undoubtedly one of the world’s biggest challenges in the 21st century. Drought risk analysis, forecasting and assessment are facing rapid expansion, not only from theoretical but also practical points of view. Accurate monitoring, forecasting and comprehensive assessments are of the utmost importance for reliable drought-related decision-making. The framework of drought risk analysis provides a unified and coherent approach to solving inference and decision-making problems under uncertainty due to climate change, such as hydro-meteorological modeling, drought frequency estimation, hybrid models of forecasting and water resource management. This Special Issue will provide researchers with a summary of the latest drought research developments in order to identify and understand the profound impacts of climate change on drought risks and water resources. The ten peer-reviewed articles collected in this Special Issue present novel drought monitoring and forecasting approaches, unique methods for drought risk estimation and creative frameworks for environmental change assessment. These articles will serve as valuable references for future drought-related disaster mitigations, climate change interconnections and food productivity impacts.

Spatial and Temporal Characteristics and Driving Forces of Vegetation Changes in the Huaihe River Basin from 2003 to 2018

In this study, MODIS normalized difference vegetation index (NDVI), TRMM3B43 precipitation, and MOD11A2 land-surface temperature (LST) data were used as data sources in an analysis of temporal and spatial characteristics of vegetation changes and ecological environmental quality in the Huaihe River basin, China, from 2003 to 2018. The Mann–Kendall (MK) non-parametric test and the Theil–Sen slope test were combined for this analysis; then, when combined with the results of the MK mutation test and two introduced indexes, the kurtosis coefficient (KU) and skewness (SK) and correlations between NDVI, precipitation (TRMM), and land-surface temperature (LST) in different time scales were revealed. The results illustrate that the mean NDVI in the Huaihe River basin was 0.54. The annual NDVImax curve fluctuations for different land cover types were almost the same. The main reasons for the decrease in or disappearance of vegetation cover in the Huaihe River basin were the expansion of towns and impact of human activities. Furthermore, vegetation cover around water areas was obviously degraded and wetland protections need to be strengthened urgently. On the same time scale, change trends of NDVI, TRMM, and LST after abrupt changes became consistent within a short time period. Vegetation growth was favored when the KU and SK of TRMM had a close to normal distribution within one year. Monthly TRMM and LST can better reflect NDVI fluctuations compared with seasonal and annual scales. When the precipitation (TRMM) is less than 767 mm, the average annual NDVI of different land cover types is not ideal. Compared with other land cover types, dry land has stronger adaptability to changes in the LST when the LST is between 19 and 22.6 °C. These trends can serve as scientific reference for protecting and managing the ecological environment in the Huaihe River basin.