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Understanding the spatiotemporal dynamics of drought and its potential effect on crop production is critical in decision-making processes to support sustainable food production under climate change. This is especially true for China, the largest producer of cereal crops in the world, where production losses could significantly impact the global food chain. Here, we utilized global remote sensing products to study the spatiotemporal dynamics of meteorological and agricultural droughts across all of China during 1982–2018 and their potential linkage with winter wheat and summer corn yield losses since 2000. Specifically, we derived the agricultural Standardized Precipitation Index (aSPI; at 1, 3, and 6-month scales) and the Standardized Vegetation Supply Water Index (SVSWI) and explored their regional-scale dynamics across four sub-regions of China. Results suggest that the frequency of extreme drought events has increased in China since 2000, mostly affecting the winter wheat growing season (i.e., December–May) across North China. This increased frequency of drought events followed consistent spatiotemporal patterns of 3-month aSPI, mostly affecting Northwest/North China, Qinghai-Tibet, and South China during April, December, and May, respectively. The 3-month aSPI and SVSWI anomalies showed a good correlation with winter wheat yield anomaly across the Qinghai-Tibet area and corn yield across South China. The summer corn yield across all sub-regions and winter wheat yield across some agricultural zones in North China showed significant decreasing trends since 2000. This study provides a useful understanding of the spatiotemporal dynamics of agricultural drought and its potential implications for crop production in China. [Display omitted] • Precipitation and vegetation index-based drought products were derived for China. • Extreme drought and wet event frequency increased over the twenty first century. • The winter wheat growing season in the North was mostly affected by drought. • Crop yield anomaly showed positive correlation with drought indices. • Wheat yield showed increasing (North) and decreasing (South) trends. [ABSTRACT FROM AUTHOR]

The feedback mechanism of cotton yield to climate change is important knowledge for cotton planting planning and stable production. However, the impact of sowing date on cotton yield in China's main cotton producing areas under future climate change is still unclear. To deal with it, this study first evaluated the applicability of the AquaCrop model using actual observed cotton growth and yield data. Then, based on the 27 Global Climate Models (GCMs) multi-model ensemble data in the Sixth Coupled Model Intercomparison Project (CMIP6), the future climate changes in China 's main cotton producing areas from 2021 and 2060 (2040 s) and 2061–2100 (2080 s) under two typical Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5–8.5 were predicted. Finally, the effects of different sowing dates on cotton yield under SSP2–4.5 and SSP5–8.5 scenarios were simulated. The results showed that: (1) AquaCrop model had good applicability and it could accurately simulate cotton growth and yield. (2) The temperature under the SSP2–4.5 and SSP5–8.5 scenarios increased significantly compared with the baseline period (1985–2020). Precipitation during the cotton growth period mainly showed an increasing trend, but the spatial and temporal variability was large. The reference crop evapotranspiration (ET o) during the cotton growth period mainly showed a decreasing trend under the SSP2–4.5 (2040 s) scenario, while it mainly showed an increasing trend in other future periods. (3) The cotton yield under the SSP2–4.5 and SSP5–8.5 scenarios increased by an average of 10.51% and 13.67%, 14.76% and 28.04% in 2040 s and 2080 s compared to the baseline period (1981–2015), respectively. (4) Early sowing was beneficial to increase cotton yield, cotton yield sown on March 31 increased by an average of 5.32% compared to May 10. In conclusion, future climate change will have positive impacts on cotton production in China, and early sowing is an effective adaptation measure to cope with climate change. • AquaCrop model has good applicability in the main cotton producing areas in China. • The simulation of future yield of cotton is based on the latest CMIP6 data. • Future climate change have positive impacts on cotton production in China. • Early sowing is an effective adaptation measure to cope with climate change. [ABSTRACT FROM AUTHOR]

Abstract\nNovelty statementThe agro-waste derived valuable products are prime interest for effective management of toxic heavy metals (THMs). The present study investigated the efficacy of biochars (BCs) on immobilization of THMs (Cr, Zn, Pb, Cu, Ni and Cd), bioaccumulation and health risk. Agro-wastes derived BCs including wheat straw biochar (WSB), orange peel biochar (OPB), rice husk biochar (RHB) and their composite biochar (CB) were applied in industrial contaminated soil (ICS) at 1% and 3% amendments rates. All the BCs significantly decreased the bioavailable THMs and significantly (p < 0.001) reduced bioaccumulation at 3% application with highest efficiency for CB followed by OPB, WSB and RHB as compared to control treatment. The bioaccumulation factor (BAF), concentration index (CI) and ecological risk were decreased with all BCs. The hazard quotient (HQ) and hazard index (HI) of all THMs were <1, except Cd, while carcer risk (CR) and total cancer risk index (TCRI) were decreased through all BCs. The overall results depicted that CB at 3% application rate showed higher efficacy to reduce significantly (p < 0.001) the THMs uptake and reduced health risk. Hence, the present study suggests that the composite of BCs prepared from agro-wastes is eco-friendly amendment to reduce THMs in ICS and minimize its subsequent uptake in vegetables.The present study has a scientific research scope, based on reduction of bioavailability and bioaccumulation of toxic heavy metals (THMs) by the addition of biochars derived from agro-wastes and their composite biochar (CB), thereby decreasing the potential health risk. Limited study has been conducted, especially on the impact of CB in THMs-contaminated soil. This study could fill the scientific research gap and provides useful information for mitigation of THMs present in contaminated soil, which could be followed by the Environmental Protection Agency, Ministry of Agriculture and farmers in degraded lands. [ABSTRACT FROM AUTHOR]

Increasing frequency and intensity of extreme drought events have harmed the environment, ecosystem, and agricultural productivity. However, the characteristics of agricultural drought in China have not been well understood. The remote sensing (RS) based gridded monthly precipitation, soil moisture, land surface temperature (LST), and normalized difference vegetation index (NDVI) datasets over 1982–2018 were utilized to derive standardized precipitation index (SPI), standardized soil moisture index (SSI), multivariate standardized drought index (MSDI), and vegetation health index (VHI). The variation patterns and trends of SPI, SSI, and MSDI at the 1-, 3-, and 6-month timescales against monthly VHI anomaly were compared to identify the best agricultural drought index in China. The drought variations in the four sub-regions (northwest, north, Qinghai-Tibet area, and south area) were also investigated. The results showed that: (1) Temporal patterns of VHI anomaly were similar to relative soil moisture and slightly different from precipitation. The spatial patterns of MSDI matched with VHI the best than SPI and SSI. (2) All three indices showed positive correlations with VHI at the three timescales. The highest correlation coefficients (r) between MSDI and VHI ranged from 0.25 to 0.67, 0.22 to 0.78, 0.23 to 0.69, and 0.19 to 0.74 in northwest China, north China, Qinghai-Tibet Plateau, and south China, respectively. (3) The connections between monthly VHI and the three drought indices were weaker at the 1-month timescale (0.16 < r < 0.25) than the 3-month (0.39 < r < 0.78) and 6-month (0.26 < r < 0.68) timescales. (4) The VHI significantly increased in most of China except north China. Overall, MSDI showed better performance for monitoring agricultural drought in China's mainland. Unlabelled Image • The RSM and VHI had similar temporal pattern, slightly different from precipitation. • MSDI against VHI performed well compared to SPI, and SSI. • The correlations among pairs of SPI, SSI, and MSDI were higher than with VHI. • The VHI showed significantly increasing trend in all sub-regions except north China. [ABSTRACT FROM AUTHOR]

Affected by global warming, the frequency of crop pests and diseases have increased, causing huge losses to agricultural production. To better grasp the development and trends of research on the effects of climate change on crop pests and diseases, the literature on the impact of climate change on crop pests and diseases published from 1990 to 2021 in the Web of Science (WOS) core collection database was used. This study explores the literature characteristics and hotspot evolution through the bibliometric visualization analysis software COOC, VOSviewer, and CiteSpace, with a view to identifying the changing characteristics and trends of research changes in this field. The results showed that the number of literature on the impact of climate change on crop pests and diseases increased rapidly. The main fields involved include environmental sciences, ecology, and agronomy. Papers in these fields mainly published in journals, such as PLos One, Forest Ecology and Management, and Frontiers in Plant Science. The country with the highest number of publications was the United States, followed by China and Australia. The most prolific authors in the top 20 are research scholars from China. The first author of the top 20 highly cited papers was from the United States. It was found that that current research on the impact of climate change on crop pests and diseases mainly focuses on agricultural production and food safety. Modelling and crop growth has maintained steady development. At present, research in this field mainly focuses on pest management strategies under the impact of climate change, the response of single species, and the complex ecological mechanisms behind the response. This study provides unique insights into the research field of the impact of climate change on crop pests and diseases and provides a reference direction for future research development in this field. [ABSTRACT FROM AUTHOR]

Droughts are one of the world's most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982–2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1–12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI–12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1–12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008–2010, 2000–2003, and 1984–1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d'Ivoire, Burkina Faso, Niger, and Nigeria. [ABSTRACT FROM AUTHOR]

The unpredictable precipitation and water deficit conditions in semiarid regions significantly reduce the yield of summer maize. The exogenous application of plant growth regulators can be used as a strategy to enhance plant stress tolerance and improve the growth and yield of maize under semiarid conditions. Here, we studied the protective role of melatonin application on maize yield using grain filling rate and hormonal crosstalk in maize grains. In the first field experiment, seeds were soaked with melatonin at a concentration of 0 (SM0), 25 (SM1), 50 (SM2), and 75 µM (SM3) µM. In contrast, in the second experiment, melatonin was applied on the foliage at the ninth leaf stage at a concentration of 0 (FM0), 25 (FM1), 50 (FM2), and 75 (FM3) µM. Our findings showed that melatonin treatments as seed soaking significantly increased single seed weight, seed filling rate in superior, medium and inferior seeds by regulating the hormone levels compared to foliar application. Application of melatonin significantly increased the zeatin+zeatin riboside (Z+ZR), indole-3-acetic acid (IAA), and gibberellic acid (GA) contents. However, it significantly inhibited the contents of abscisic acid (ABA) during the seed filling period. The content of Z+ZR, IAA, and GA was positively correlated with the maximum seed filling rate, seed weight, and mean filling rate in middle, superior and lower seeds, while the ABA was negatively correlated. The ABA content in inferior seeds was positively correlated with the maximum and mean seed filling rate. In semiarid regions, melatonin treatment of SM2 and FM2 significantly increased the dry matter per plant, 100-grain weight, seed filling rate, IAA, Z+ZR, GA contents, ear characteristics, and maize yield. [ABSTRACT FROM AUTHOR]

In the context of climate change, hydrological processes between surface water and groundwater become increasingly complex, which may aggravate the risk of hydrological drought. As the complexity of factors influencing drought increases, traditional univariate drought indices are likely to be insufficient for accurate drought analysis. Therefore, it is important to develop an appropriate index combining surface water and groundwater for comprehensive monitoring of hydrological drought, and to understand the process of drought propagation to reduce the impact of droughts. This study develops the standardized runoff groundwater drought index (SRGI), which is a multi-variable model based on a combination of the standardized groundwater level drought index (SGI) and the standardized runoff drought index (SRI). The cross-wavelet transforms and correlation coefficient methods are used to reveal the linkages and propagation characteristics between meteorological and hydrological drought. The results indicate that (1) the generalized extreme value (GEV) distribution is generally suitable for the SGI calculation in the midstream of the Heihe River Basin (HHRB); (2) the SRGI, combining the advantages of the SRI and SGI, can detect droughts more accurately and timely than univariate drought indices, and it demonstrates a drought warning capability to some extent; (3) the lag time of the SRI/SRGI responding to the SPI notably varied with seasons, the longer in spring and winter and the shorter in summer and autumn. Additionally, the propagation time from the SRI to the SPI is shorter than that from the SRGI to the SPI in spring and autumn, the opposite is true in summer. (4) Positive correlations exist between the SRI/SRGI and the SPI during the period 1986–2010, which are relatively stable over longer periods, but ambiguous over shorter periods. [ABSTRACT FROM AUTHOR]

Melatonin is an important plant growth regulator which plays a key role in plant growth and development. The objective of the current research was to evaluate the effect of foliar application of melatonin (MF) on photosynthetic efficiency, antioxidant defense mechanism, and its relation with leaf senescence in maize crop grown in a semi-arid region. A field experiment was conducted during 2017 and 2018 growth season, where melatonin was applied to the foliage at concentrations of 0 (MF0), 25 (MF1), 50 (MF2), and 75 (MF3) μM at the ninth leaf stage. Foliar application of melatonin significantly improved chlorophyll content, net photosynthetic rate, soluble sugar content, and soluble protein content during the process of leaf senescence. The application of melatonin also enhanced antioxidant enzyme activities including superoxide dismutase, catalase, and peroxidase, while reduced malondialdehyde and reactive oxygen species accumulation. Melatonin foliar application also increased total leaf area per plant, grains per ear, thousand grain weight and grain yield of maize crop in a semi-arid region. The application of melatonin significantly improved photosynthetic activity, antioxidant defense mechanism, and yield of maize crop in a semi-arid region, where the most effective treatment was MF2. [ABSTRACT FROM AUTHOR]

Probabilistic modelling of drought events is a significant aspect of water resources management and planning. In this study, popularly applied and several relatively new bivariate Archimedean copulas were employed to derive regional and spatial based copula models to appraise drought risk in mainland China over 1961–2013. Drought duration ( Dd ), severity ( Ds ), and peak ( Dp ), as indicated by Standardized Precipitation Evapotranspiration Index (SPEI), were extracted according to the run theory and fitted with suitable marginal distributions. The maximum likelihood estimation (MLE) and curve fitting method (CFM) were used to estimate the copula parameters of nineteen bivariate Archimedean copulas. Drought probabilities and return periods were analysed based on appropriate bivariate copula in sub-region I–VII and entire mainland China. The goodness-of-fit tests as indicated by the CFM showed that copula NN19 in sub-regions III, IV, V, VI and mainland China, NN20 in sub-region I and NN13 in sub-region VII are the best for modeling drought variables. Bivariate drought probability across mainland China is relatively high, and the highest drought probabilities are found mainly in the Northwestern and Southwestern China. Besides, the result also showed that different sub-regions might suffer varying drought risks. The drought risks as observed in Sub-region III, VI and VII, are significantly greater than other sub-regions. Higher probability of droughts of longer durations in the sub-regions also corresponds to shorter return periods with greater drought severity. These results may imply tremendous challenges for the water resources management in different sub-regions, particularly the Northwestern and Southwestern China. [ABSTRACT FROM AUTHOR]

The effects of global warming on vegetation phenology are widespread. A study in Southern Khyber Pakhtunkhwa, Pakistan investigated the effects of ecological factors including precipitation and temperature on phytochemical, and nutritional composition of wild Caralluma tuberculata N. E. Brown. Photosynthetic pigments, saponins, crude proteins, crude fats, moisture, and ash were significantly higher (p < 0.05) in the plants of Shaker Darra area than in the plants of Lakki Marwat, Darra Adam Khel, and Spin Wam areas. Pearson correlation explains a high correlation existing between the annual rainfall and temperature with that of pregnane glycosides. The current study concluded that mainly rainfall influences the phytochemical, nutritional, and physiological profiles of C. tuberculata. • C. tuberculata , a locally vulnerable multipurpose plant species. • Rainfall affects photosynthetic pigments and phyto-chemicals of C. tuberculata. • Temperature also alters phyto-chemical and nutritional composition. [ABSTRACT FROM AUTHOR]

Biochar application improves soil environment and thus enhances crop yields. However, its optimum application rate is site- and crop-specific. Our aim is to determine the optimum biochar application rate (OBAR) in sugar beet by investigating its influences on economic profit. A 3-year field biochar application test for sugar beet was conducted in Xinjiang, China. Plastic film mulch and drip irrigation were applied. Five treatments of biochar application rates were made. For each treatment, same biochar rates (i.e. 0, 10, 25, 50 and 100 t ha−1) were applied in each year from 2018 to 2020 with a few exceptions where biochar rates were manipulated according to the results of the first two years. The physiological ecology indicators, irrigation water and fertilizer productivities and economic profit were analyzed. The N, P and K uptake during growth stages of sugar beet, the physiological ecology indicators (e.g. aboveground and underground biomass, leaf area index) and sugar content increased with increase in biochar application rate. The irrigation water and partial fertilizer productivities and economic benefits were the greatest at the biochar application of 10 t ha−1 for each year, which was recommended as the best dose for sugar beet planting. For sugar beet planting in arid and semi-arid zone, continuous application with OBAR of 10 t ha−1 had the maximal economical profit, the net profit increase was 6.94 billion dollars compared with no biochar application. Therefore, considering the comprehensive responses of the nutrient uptake, salt absorption, yield, and the economic benefit of various biochar application rate, the biochar application rate of 10 t ha−1 year−1 was recommended as the optimum dose. There were complicated interactions of soil, biochar and crops, further studies involving multi-discipline overlap are needed to reveal the yield, quality and productivity increase mechanics of sugar beet affected by biochar. • The biochar applications increased the N, P and K uptake during different growth stages of sugar beet. • The growth and yield related indicators were significant increased under biochar application conditions. • Yield components were the largest under biochar application rate of 10 t ha−1. • The biochar application rate of 10 t ha−1 was recommended as the optimum dose for sugar beet planting. [ABSTRACT FROM AUTHOR]

Climate change may have detrimental effects on income from crop farming in Pakistan and according to the IPCC (2014), the impacts of climate change will be more vigorous in the coming years. Despite a few studies we still have limited knowledge on how climate change can impact the crop farming economy in Pakistan. Considering Khyber Pakhtunkhwa (KP) province of Pakistan as the study area, we attempted to fill this gap by analyzing the economic impacts of climate change on the net income from crop farming. Data from the 635 farm households and also climatic variables data were collected from five agro-ecological zones (AEZ) of the KP. The Ricardian method was employed for analysis purposes. The results revealed that the increase of annual average temperature and decreases in rainfall is strongly associated with net revenue deficits. The effects of increasing precipitation are observed to be useful but an increase in temperature is forecasted to be harmful to the net revenue. The effect of increased temperature in the summer season on NR ranged between US$-29 to US$35, and the effect in the winter season ranged between US$-14 to US$28 per hectare. The effect of decreased annual precipitation on NR in the summer season and winter season ranged between US$ 0 to US$-24 and US$<-20 per hectare, respectively. This effect is particularly severe in the Central Valley Plains, Southern Piedmont Plains, and Western Dry Mountains. This study further evaluates the effects of the climatic forecasts by three Atmospheric Oceanic General Circulation Models (AOGCM). Our results will facilitate the policymakers to implement suitable strategies in KP to curb the deleterious effects of climate change on overall crop production and farmer income. [ABSTRACT FROM AUTHOR]

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification. [ABSTRACT FROM AUTHOR]

Studying the significant impacts of drought on vegetation is crucial to understand its dynamics and interrelationships with precipitation, soil moisture, and temperature. In North and West Africa regions, the effects of drought on vegetation have not been clearly stated. Therefore, the present study aims to bring out the drought fluctuations within various types of Land Cover (LC) (Grasslands, Croplands, Savannas, and Forest) in North and West Africa regions. The drought characteristics were evaluated by analyzing the monthly Self-Calibrating Palmer Drought Severity Index (scPDSI) in different timescale from 2002 to 2018. Then, the frequency of droughts was examined over the same period. The results have revealed two groups of years (dry years and normal years), based on drought intensity. The selected years were used to compare the shifting between vegetation and desert. The Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Precipitation Condition Index (PCI), and the Soil Moisture Condition Index (SMCI) were also used to investigate the spatiotemporal variation of drought and to determine which LC class was more vulnerable to drought risk. Our results revealed that Grasslands and Croplands in the West region, and Grasslands, Croplands, and Savannas in the North region are more sensitive to drought. A higher correlation was observed among the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Tropical Rainfall Measuring Mission (TRMM), and Soil Moisture (SM). Our findings suggested that NDVI, TRMM, and SM are more suitable for monitoring drought over the study area and have a reliable accuracy (R2 > 0.70) concerning drought prediction. The outcomes of the current research could, explicitly, contribute progressively towards improving specific drought mitigation strategies and disaster risk reduction at regional and national levels. [ABSTRACT FROM AUTHOR]

Drought is one of the main factors that impacts crop yields. However, determining how different drought types affect winter wheat yields is difficult due to the lack of observation data. This work aimed to investigate the impacts of multi-timescale droughts on winter wheat yields. The winter wheat yields during 1981–2015 were simulated by the DSSAT-CERES-Wheat model. We analyzed the drought characteristics based on the standardized precipitation evapotranspiration index (SPEI) and soil moisture deficit index (SMDI) at timescales of 1 to 9 months at 108 sites in China. The modified Mann-Kendall (MMK) method was used to test the tendency of 1-, 3- and 6-month SPEI, SMDI and winter wheat yields. Pearson correlation analysis was used to explore the relationship between winter wheat yields and SPEI/SMDI at different timescales. The results showed that, DSSAT-CERES-Wheat generally performed well in simulating winter wheat anthesis date, maturity date and yields (0.64 < R 2 < 0.97, where R 2 is determination of coefficient). The dry or wet status for the 1- to 9-month timescales of SPEI and SMDI were generally consistent in the three subregions. Seasonal drought occurred more frequently in the Huang-Huai-Hai Plain than in the other two subregions. The 4-month SPEI and 1-month SMDI at the 0–10 cm depth affected the winter wheat yield more during the jointing to milk stages. For yields vs. 4-month SPEI, the number of stations with Pearson correlation coefficient r > 0.4 in March, April and May was 29, 35 and 23, respectively. For yields vs. 1-month SMDI, the number of stations with r > 0.4 in March, April and May was 16, 23 and 33, respectively. SMDI and SPEI explained more than 14% and less than 2% of the yield variability, respectively. Closer relations between SMDI and winter wheat yields were observed. This study provides useful references for preventing agricultural drought. • DSSAT-CERES-Wheat performed generally well for simulating winter wheat anthesis date, maturation date and yields. • The 4-month SPEI and 1-month SMDI at 0–10 cm depth affected winter wheat yields greater during jointing to milk stages. • SMDI was better than SPEI in denoting effects of drought on winter wheat yields. • This study obtained key timescales of SPEI and SMDI and key growth periods of winter wheat. [ABSTRACT FROM AUTHOR]

In the present study, we examined the potential role of paclobutrazol in delaying leaf senescence, in causing changes in the activities of antioxidants, and in the maintenance of photosynthetic activity during the senescence process, and, therefore, on the grain yield of maize under semiarid field conditions. Maize seeds were pretreated with 0 (CK), 200 (PS1), 300 (PS2), and 400 (PS3) mg paclobutrazol L−1. Our results indicated that elevated levels of reactive oxygen species (ROS) and higher accumulation of malondialdehyde (MDA) contents were positively associated with accelerated leaf senescence during the grain-filling periods. The leaf senescence resulted in the disintegration of the photosynthetic pigments and reduced the net photosynthetic rate after silking. However, the resultant ROS burst (O2− and H2O2) was lessened and the leaf senescence and chlorophyll degradation were evidently inhibited in leaves of paclobutrazol-treated maize plants, which was strongly linked with upregulated activities of antioxidant enzymes in treated plants. The enhanced chlorophyll contents and availability of a greater photosynthetic active green leaf area during the grain filling period facilitated the maintenance of higher photosynthetic rate, and light-harvesting efficiency of photosynthesis associated with photosystem II (PSII) resulted in higher kernel number ear−1 and thousand kernel weights, and thus increased the final grain yield. The average maize grain yield was increased by 18.8% to 55.6% in paclobutrazol treatments, compared to untreated control. Among the various paclobutrazol treatments, PS2 (300 mg L−1) treatment showed the most promising effects on enhancing the activities of antioxidative enzymes, delaying leaf senescence and improving the yield of maize. Thus, understanding this effect of paclobutrazol on delaying leaf senescence introduces new possibilities for facilitating yield improvement of maize under semiarid conditions. [ABSTRACT FROM AUTHOR]