The rhizospheric transformation and bioavailability of mercury in pepper plants are influenced by selected Chinese soil types.

Understanding and prediction of mercury (Hg) phytoavailability in vegetable-soil systems is essential for controlling food chain contamination and safe vegetable production as Hg-contaminated soils pose a serious threat to human health. In this study, four typical Chinese soils (Heilongjiang, Chongqing, Yunnan, and Jilin) with varied physicochemical properties were spiked with HgCl2 to grow sweet pepper (Capsicum annuum L.) in a pot experiment under greenhouse condition. The chemical fractionation revealed a significant decrease in exchangeable Hg, while an increase in organically bound Hg in the rhizosphere soil (RS) compared to bulk soil (BS). This observation strongly highlights the vital role of organic matter on the rhizospheric Hg transformation irrespective of contamination levels and soil properties. Stepwise multiple linear regression (SMLR) analysis between Hg concentration in plants, Hg fractions in RS and BS, and soil properties showed that Hg in plant parts was significantly influenced by soil total Hg (THg) (R2 = 0.90), soil clay (R2 = 0.99), amorphous manganese oxides (amorphous Mn) (R2 = 0.97), amorphous iron oxides (amorphous Fe) (R2 = 0.70), and available Hg (R2 = 0.97) in BS. Nevertheless, in the case of RS, Hg accumulation in plants was affected by soil THg (R2 = 0.99), amorphous Mn (R2 = 0.97), amorphous Fe oxides (R2 = 0.66), soil pH, and organically bound Hg fraction (R2 = 0.96). Among all the evaluated soils (n = 04), metal (mercury) concentration in terms of plant uptake was reported highest in the Jilin soil. Based on SMLR analysis, the results suggested that the phytoavailability of Hg was mainly determined by THg and metal oxides regardless of the rhizospheric effect. These findings facilitate the estimation of Hg phytoavailability and ecological risk that may exist from Hg-contaminated areas where pepper is the dominant vegetable.

Mercury fractionation, bioavailability, and the major factors predicting its transfer and accumulation in soil-wheat systems.

Soil mercury (Hg) and its bioaccumulation in food crops have attracted widespread concerns globally due to its harmful effects on biota. However, soil mercury fractionation, bioavailability, and the major factors predicting its transfer and accumulation in soil-wheat-systems have not been thoroughly explored. Twenty-one (21) soil samples collected throughout China with a wide spectrum of physico-chemical characteristics were contaminated with HgCl2 and winter wheat (Triticum aestivum L.) was grown on the soils in a greenhouse pot-culture experiment for 180 days. A four-step sequential extraction was used segregating soil Hg into water-soluble (F1, 0.21 %), exchangeable (F2, 0.07 %), organically bound (F3, 16.40 %), and residual fractions (F4, 83.32 %). Step-wise multiple linear regression (SMLR) and path analysis (PA) were used to develop a prediction model and identify the major controlling factors of soil-wheat Hg transference. The SMLR results revealed that wheat Hg in leaves, husk, and grain was positively correlated with soil total and available Hg, and crystalline manganese (Cryst-Mn), while negatively correlated with soil pH, amorphous manganese (Amor-Mn) and crystalline aluminium (Cryst-Al). Bioconcentration factor (BCF) values were significantly higher in acidic soils (highest 0.05), with phytotoxic effects in some soils, as compared to alkaline soils (lowest 0.006). Furthermore, wheat grain Hg was significantly correlated with total (R2 = 0.25), water-soluble (R2 = 0.54) and NH4Ac-extractable Hg (R2 = 0.43) while also had a good correlation with soil pH (R2 = -0.20). In conclusion, the soil total and available Hg (water-soluble + exchangeable fraction), pH, organic matter, and Amor-Mn are the most important soil variables that support Hg uptake in the wheat plants, which benefit managing Hg-enriched agricultural soils for safe wheat production.

Phytoavailability and transfer of mercury in soil-pepper system: Influencing factors, fate, and predictive approach for effective management of metal-impacted spiked soils.

Mercury (Hg) contamination and accumulation in food crops is a global threat posing potential health risk to humans. However, Hg phytoavailability in soil-pepper system and its influencing factors largely remain unknown. In this study, a greenhouse pot experiment was conducted to grow peppers using 21 Chinese agricultural soils with varied soil properties and aged Hg levels. Mercury concentration in pepper leaves and fruits ranged from 0.021 to 0.057 mg kg-1 and 0.005-0.022 mg kg-1 respectively, while fruit Hg content in three soils (Anhui, Hubei, Beijing) exceeded the safety limit. Fruit Hg concentration was better positively correlated with soil Mg(NO3)2-extractable Hg content (r = 0.7, P < 0.0001) than soil total Hg content (r = 0.45, P < 0.0001). Highest bioconcentration factor (BCF, ratio of Hg plant to Hg soil) yielded in acidic soils, while the lowest BCF occurred in alkaline soils. Path analysis indicated available-Hg (R2 = 0.40) and total-Hg (R2 = 0.40) had direct positive effects on the pepper fruit Hg concentration, while direct negative effects including pH (R2 = -0.86), organic matter (R2 = -0.7), crystalline-Fe (R2 = -0.68). Those agreed with the stepwise multiple linear regression analysis which yielded a regression predictive model (R2 = 0.73, P < 0.0001). Soil available-Hg, total-Hg, pH, organic matter and crystalline-Fe & Mn were the most influencing factors of Hg phytoavailability. These results provide new insights into the phytoavailability of Hg in soil-pepper system, thus facilitating the management of pepper cultivation in Hg-enriched soils.

Assessing climate change impacts on pearl millet under arid and semi-arid environments using CSM-CERES-Millet model.

Climate change adversely affects food security all over the world, especially in developing countries where the increasing population is confronting food insecurity and malnutrition. Crop models can assist stakeholders for assessment of climate change in current and future agricultural production systems. The aim of this study was to use of system analysis approach through CSM-CERES-Millet model to quantify climate change and its impact on pearl millet under arid and semi-arid climatic conditions of Punjab, Pakistan. Calibration and evaluation of CERES-Millet were performed with the field observations for pearl millet hybrid 86M86. Mid-century (2040-2069) climate change scenarios for representative concentration pathway (RCP) 4.5 and RCP 8.5 were generated based on an ensemble of selected five general circulation models (GCMs). The model was calibrated with optimum treatment (15-cm plant spacing and 200 kg N ha-1) using field observations on phenology, growth and grain yield. Thereafter, pearl millet cultivar was evaluated with remaining treatments of plant spacing and nitrogen during 2015 and 2016 in Faisalabad and Layyah. The CERES-Millet model was calibrated very well and predicted the grain yield with 1.14% error. Model valuation results showed that there was a close agreement between the observed and simulated values of grain yield with RMSE ranging from 172 to 193 kg ha-1. The results of future climate scenarios revealed that there would be an increase in Tmin (2.8 °C and 2.9 °C, respectively, for the semi-arid and arid environment) and Tmax (2.5 °C and 2.7 °C, respectively, for the semi-arid and arid environment) under RCP4.5. For RCP8.5, there would be an increase of 4 °C in Tmin for the semi-arid and arid environment and an increase of 3.7 °C and 3.9 °C in Tmax, respectively, for the semi-arid and arid environment. The impacts of climate changes showed that pearl millet yield would be reduced by 7 to 10% under RCPs 4.5 and 8.5 in Faisalabad and 10 to 13% in Layyah under RCP 4.5 and 8.5 for mid-century. So, CSM-CERES-Millet is a useful tool in assessing the climate change impacts.

Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan.

Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha-1, respectively. Model was further used to assess climate variability. Historical climate data (1980-2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha-1 for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region.

Performance of four crop model for simulations of wheat phenology, leaf growth, biomass and yield across planting dates.

Robustness of four wheat simulation model were tested with 2-year field experiments of three cultivars across a wide range of sowing dates in two different climatic regions: Faisalabad (semi-arid) and Layyah (arid), in Punjab-Pakistan. Wheat growing season temperature ranged from -0.1°C to 43°C. The wide series of sowing dates was a unique opportunity to grow the wheat in an environment which temperatures varies from -0.1°C to 43°C. The CERES-Wheat, Nwheat, CROPSIM-Wheat and APSIM-Wheat model were calibrated against the least-stressed treatment for each wheat cultivar. Overall, the four models described performance of early, optimum and late sown wheat well, but poorly described yields of very late planting dates with associated high temperatures during grain filling. The poor accuracy of simulations of yield for extreme planting dates point to the need to improve the accuracy of model simulations at the high end of the growing temperature range, especially given the expected future increases in growing season temperature. Improvement in simulation of maximum leaf area index of wheat for all models is needed. APSIM-Wheat only poorly simulated days to maturity of very and extremely late sown wheat compared to other models. Overall, there is a need of improvement in function of models to response high temperature.

Potential impacts of climate change and adaptation strategies for sunflower in Pakistan.

Growth, development, and economic yield of agricultural crops rely on moisture, temperature, light, and carbon dioxide concentration. However, the amount of these parameters is varying with time due to climate change. Climate change is factual and ongoing so, first principle of agronomy should be to identify climate change potential impacts and adaptation measures to manage the susceptibilities of agricultural sector. Crop models have ability to predict the crop's yield under changing climatic conditions. We used OILCROP-SUN model to simulate the influence of elevated temperature and CO2 on crop growth duration, maximum leaf area index (LAI), total dry matter (TDM), and achene yield of sunflower under semi-arid conditions of Pakistan (Faisalabad, Punjab). The model was calibrated and validated with the experimental data of 2012 and 2013, respectively. The simulation results showed that phenological events of sunflower were not changed at higher concentration of CO2 (430 and 550 ppm). However LAI, achene yield, and TDM increased by 0.24, 2.41, and 4.67% at 430 ppm and by 0.48, 3.09, and 9.87% at 550 ppm, respectively. Increased temperature (1 and 2 °C) reduced the sunflower duration to remain green that finally led to less LAI, achene yield, and TDM as compared to present conditions. However, the drastic effects of increased temperature on sunflower were reduced to some extent at 550 ppm CO2 concentration. Evaluation of different adaptation options revealed that 21 days earlier (as compared to current sowing date) planting of sunflower crop with increased plant population (83,333 plants ha-1) could reduce the yield losses due to climate change. Flowering is the most critical stage of sunflower to water scarcity. We recommended skipping second irrigation or 10% (337.5 mm) less irrigation water application to conserve moisture under possible water scarce conditions of 2025 and 2050.