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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]

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]

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]

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]

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]