Influence of agro-wastes derived biochar and their composite on reducing the mobility of toxic heavy metals and their bioavailability in industrial contaminated soils.

The 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.

Melatonin improves the seed filling rate and endogenous hormonal mechanism in grains of summer maize.

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.

Monitoring agricultural drought in Peshawar Valley, Pakistan using long -term satellite and meteorological data.

Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.

An impact of climate change and groundwater salinity on shadow price of water, farmers' revenue, and socioeconomic and environmental indicators in district Kohat-Pakistan.

Globally, agricultural productivity is adversely impacted due to climatic changes as the temperatures rises and precipitation decreases, and especially in Pakistan, which ultimately enhanced groundwater salinity and harmed water quality in the country. However, the impacts of groundwater salinity and climate change on farmers' revenue have not been fully understood in Pakistan. Therefore, the focus of current research is the assessment of shadow price of water, farmers' revenue, and socioeconomic and environmental indicators affected by variations in groundwater salinity, precipitation, and temperature. The estimation of crop yield sensitivity to groundwater salinity, precipitation, and temperature and their prediction for 2030, 2040, and 2050 time periods was accomplished through the technique of General Maximum Entropy and Response-Yield function. Moreover, the assessment of groundwater quality and climate variable impacts on socioeconomic and environmental indicators was obtained through Target Motad-PMP model. In the end, the most suitable climate change scenario in the study area was established by applying a multi-criteria decision-making method. The results revealed that groundwater salinity and temperature expressed a significantly increasing trend with the Z values of 5.82 and 2.15, respectively. While the precipitation depicted a significantly decreasing trend (Z value = -3.37). The negative impact of climatic changes and groundwater salinity was revealed for revenue risk and shadow prices of water. The most negative impact on income risk and shadow prices is during 2050 horizon with a decrease by 11.4 and 19.4% respectively. The environmental index is the most important with a priority of 43.4% compared to the socio-economic indicators. The sub-index water use is also significant in the study area with a priority of 28.1%. A2 is the most appropriate climate scenario conferring to the TOPSIS ranking method. Therefore, the A2 scenario should be taken into account for the policy of adaptation to the climate change wonder in district Kohat.

Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China.

Drought is a major natural disaster that significantly impacts the susceptibility and flexibility of the ecosystem by changing vegetation phenology and productivity. This study aimed to investigate the impact of extreme climatic variation on vegetation phenology and productivity over the four sub-regions of China from 2000 to 2017. Daily rain gauge precipitation and air temperature datasets were used to estimate the trends, and to compute the standardized precipitation-evapotranspiration index (SPEI). Remote sensing-based Enhanced Vegetation Index (EVI) data from a moderate resolution imaging spectroradiometer (MODIS) was used to characterize vegetation phenology. The results revealed that (1) air temperature had significant increasing trends (P < 0.05) in all sub-regions. Precipitation showed a non-significant increasing trend in Northwest China (NWC) and insignificant decreasing trends in North China (NC), Qinghai Tibet area (QTA), and South China (SC). (2) Integrated enhanced vegetation index (iEVI) and SPEI variations depicted that 2011 and 2016 were the extremely driest and wettest years during 2000-2017. (3) Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016. In 2011, changes in the vegetation phenology with the length of the growing season (ΔLGS) = was - 14 ± 36 days. In 2016, the overall net effect changed at the onset and end of the growing season with ΔLGS of 34 ± 71 days. The change in iEVI per SPEI increased rapidly with a changing rate of 0.16 from arid (NWC, and QTA) to semi-arid (NWC, QTA and NC) and declined with a rate of - 0.04 from semi-humid (QTA, NC, and SC) to humid (SC) region. A higher association was observed between iEVI and SPEI as compared to iEVI and precipitation. Our finding exposed that north China is more sensitive to climatic variation.

Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data.

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.

Drought evolution indicated by meteorological and remote-sensing drought indices under different land cover types in China.

Remote sensing freely provides many processed image products such as moderate resolution imaging spectroradiometer (MODIS), and long-term data record (LTDR), for the investigation of drought evolution. Our objectives are to investigate drought evolution and spatiotemporal variations from 1982 to 2017 based on two remote-sensing indices, namely, the normalized difference vegetation index (NDVI) and the vegetation condition index (VCI), and a popular meteorological index-standardized precipitation index (SPI)-under four different land cover types, cropland, forestland, grassland, and desertland in China. The modified Mann-Kendall test was used to detect the significance of a trend. The Pearson correlation method was used to find the relationship between NDVI anomaly, VCI, precipitation, and SPI. The results revealed that (a) both mean monthly and yearly precipitation had a general land cover type rank of forestland > grassland ≈ cropland > desertland. (b) A positive correlation was found between drought indices (NDVI anomaly, VCI, SPI) and precipitation for different land cover types. The NDVI anomaly and VCI were well correlated with 3-month SPI for cropland and were well correlated with 6-month SPI for forestland. VCI performed better than NDVI anomaly when correlating with SPI. (c) The coefficient of determination (R2) was obtained for precipitation and VCI in the driest (2011) and wettest (2016) years. The R2 values for desert and grassland ranged from 0.70 to 0.90 and for cropland and forestland were lower (0.54-0.69). (d) Only precipitation, SPI, and VCI of cropland had significant increasing trends. The spatial distribution patterns of precipitation, NDVI, and VCI increased with the decreased elevation. The study revealed that desert and grassland had been regularly exposed to moderate or extreme droughts conditions and confirmed that desert and grassland are more sensitive to short-term drought.

Foliar application of melatonin delay leaf senescence in maize by improving the antioxidant defense system and enhancing photosynthetic capacity under semi-arid regions.

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.

Impact assessment of land use change on surface temperature and agricultural productivity in Peshawar-Pakistan.

The profound appreciation of urban expansion and land use change (LUC) considerably influences the ecosystem functions, services, and biodiversity along with the local and regional climate. Land use has undergone an awful transformation due to rapid urbanization and population growth, which in turn increased land surface temperature (LST) in district Peshawar, Pakistan. The current study tends to capture the influence of land use on LST and agricultural productivity by employing multi-temporal, multispectral satellite data and agricultural production data during the selected years, i.e., 1996, 2003, and 2016. The results demonstrated that barren land considerably decreased while the urban area increased over time in all three phases. Furthermore, significant LST difference was found in different land cover units; e.g., barren land and urban area have the maximum, while water bodies followed by vegetation retain minimum LST in all three phases, i.e., 1996, 2003, and 2016. Similarly, the results from agricultural production revealed that except for wheat crops, which decreased by 7.54% during 1999-2003, the production of all major food crops increased during the selected years. However, the production of sugar cane and barley experienced considerable reduction during the selected years, except for barley, which increased by 22.86% during 2003-2016. The finding of this study provides guidance, policy recommendations, and reference for future researchers. Graphical abstract .