Research progress in parameterizing irrigation and fertilization in land surface model.

Under the context of global climate change and growing population, irrigation and fertilization have become important ways to ensure food production, with consequences on water cycling, energy flow, and materials cycling in terrestrial ecosystems. In the land surface model (LSM), coupling irrigation and fertilization schemes are of great importance for clearly understanding the land-atmosphere interactions to ensure food security. We reviewed the expression methods of three key parameters, namely, the applied method, usage, and time in the parameterization process of irrigation and fertilization (nitrogen fertilizer) in LSM. We found that the ways to irrigate and ferti-lize in LSM are different from the ways used in actual practice due to the limitation of the high resolution of spatio-temporal data, which makes it difficult to understand the actual influences of irrigation and fertilization on grain yield, environment, and local climate. Finally, we proposed future works: 1) taking the differences of crop water demand into account and making the different irrigation thresholds for different crops to properly evaluate the total and intensity of water consumption of different crops; 2) using the field records and the regional grid data of fertilization and irrigation developed in recent years to develop parameterized schemes that are more in line with actual agricultural operations, which can accurately reveal their economic, ecological, and climatic effects; 3) developing fertilization diagnosis scheme considering crop type, phenological stage, and soil basic fertility as the supplementary scheme in LSM, to improve the applicability and simulation accuracy of LSM in the areas without nitrogen fertilizer data.

Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation.

Land surface models (LSMs) have prominent advantages for exploring the best agricultural practices in terms of both economic and environmental benefits with regard to different climate scenarios. However, their applications to optimizing fertilization and irrigation have not been well discussed because of their relatively underdeveloped crop modules. We used a CLM5-Crop LSM to optimize fertilization and irrigation schedules that follow actual agricultural practices for the cultivation of maize and wheat, as well as to explore the most economic and environmental-friendly inputs of nitrogen fertilizer and irrigation (FI), in the North China Plain (NCP), which is a typical intensive farming area. The model used the indicators of crop yield, farm gross margin (FGM), nitrogen use efficiency (NUE), water use efficiency (WUE), and soil nitrogen leaching. The results showed that the total optimal FI inputs of FGM were the highest (230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize; 137.5 ± 25 kg N ha-1 and 362.5 ± 47.9 mm for wheat), followed by the FIs of yield, NUE, WUE, and soil nitrogen leaching. After multi-objective optimization, the optimal FIs were 230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize, and 137.5 ± 25 kg N ha-1 and 387.5 ± 85.4 mm for wheat. By comparing our model-based diagnostic results with the actual inputs of FIs in the NCP, we found excessive usage of nitrogen fertilizer and irrigation during the current cultivation period of maize and wheat. The scientific collocation of fertilizer and water resources should be seriously considered for economic and environmental benefits. Overall, the optimized inputs of the FIs were in reasonable ranges, as postulated by previous studies. This result hints at the potential applications of LSMs for guiding sustainable agricultural development.

[Health Risk Assessment of Heavy Metals in Soil and Wheat Grain in the Typical Sewage Irrigated Area of Shandong Province].

Sewage irrigation is a common alternative to make up for the shortage of agricultural irrigation in intensive agricultural areas. Abundant organic matter and nutrients in sewage can improve soil fertility and crop yield, but hazardous materials, such as heavy metals, will damage the soil environmental quality and threaten human health. To better understand the characteristics of heavy metal enrichment and potential health risk in a sewage irrigated soil-wheat system, a total of sixty-three pairs of topsoil and wheat grain samples were collected from the sewage irrigated area of Longkou City in Shandong Province. The contents of Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg were determined to analyze heavy metal contamination and calculate bio-accumulation factor (BAF), estimated daily absorption (EDA), as well as hazard quotient (HQ). The results showed that the average contents of the eight heavy metals were 61.647, 30.439, 29.769, 36.538, 63.716, 8.058, 0.328, and 0.028 mg·kg-1, respectively, which all exceeded the background values of corresponding heavy metals in the eastern Shandong Province. Especially, the average content of Cd was higher than the current standard value of soil environmental quality of agricultural land soil pollution risk control, indicating the apparent soil contamination. However, the correlations between the heavy metal contents in soil and wheat grains were not significant, suggesting that it is difficult to conclude the enrichment degree of heavy metals in wheat grains merely by the heavy metal contents in soil. The results of BAF showed that the high enrichment capacity of wheat grain was primarily obtained with Zn, Hg, Cd, and Cu. According to the national food safety limit standard, the over-limit ratios of Ni (100%) and Pb (96.8%) in wheat grains were the most serious. As a result, under the current consumption of local wheat flour, the EDAs of Ni and Pb were high, accounting for 28.278% and 1.955% of the acceptable daily intakes (ADI) for adults and 131.980% and 9.124% of the ADIs for children. The results of the health risk assessment exhibited that As and Pb were the main sources causing health risks, accounting for approximately 80% of the total risk. Although the sums of the HQ of the eight heavy metals for adults and children were below 10, the total HQ of children was 1.245 times higher than that of adults. The food safety of children should receive more attention. When considering spatial characteristics, the health risk in the southern study area was higher than that in the northern part of the study area. The prevention and control of heavy metal contamination in the southern area should be strengthened in the future.

Does Climate Change Increase Crop Water Requirements of Winter Wheat and Summer Maize in the Lower Reaches of the Yellow River Basin?

With increasing water resources stress under climate change, it is of great importance to deeply understand the spatio-temporal variation of crop water requirements and their response to climate change for achieving better water resources management and grain production. However, the quantitative evaluation of climate change impacts on crop water requirements and the identification of determining factors should be further explored to reveal the influencing mechanism and actual effects thoroughly. In this study, the water requirements of winter wheat and summer maize from 1981 to 2019 in the lower reaches of the Yellow River Basin were estimated based on the Penman-Monteith model and crop coefficient method using daily meteorological data. Combined with trends test, sensitivity and contribution analysis, the impacts of different meteorological factors on crop water requirement variation were explored, and the dominant factors were then identified. The results indicated that the temperature increased significantly (a significance level of 0.05 was considered), whereas the sunshine duration, relative humidity and wind speed decreased significantly from 1981 to 2019 in the study area. The total water requirements of winter wheat and summer maize presented a significant decreasing trend (-1.36 mm/a) from 1981 to 2019 with a multi-year average value of 936.7 mm. The crop water requirements of winter wheat was higher than that of summer maize, with multi-year average values of 546.6 mm and 390.1 mm, respectively. In terms of spatial distribution patterns, the crop water requirement in the north was generally higher than that in the south. The water requirements of winter wheat and summer maize were most sensitive to wind speed, and were less sensitive to the minimum temperature and relative humidity. Wind speed was the leading factor of crop water requirement variation with the highest contribution rate of 116.26% among the considered meteorological factors. The results of this study will provide important support for strengthening the capacity to cope with climate change and realizing sustainable utilization of agricultural water resources in the lower reaches of the Yellow River Basin.

Assessment of GF3 Full-Polarimetric SAR Data for Dryland Crop Classification with Different Polarimetric Decomposition Methods.

Crop classification is one of the most important agricultural applications of remote sensing. Many studies have investigated crop classification using SAR data, while few studies have focused on the classification of dryland crops by the new Gaofen-3 (GF3) SAR data. In this paper, taking Hengshui city as the study area, the performance of the Freeman-Durden, Sato4, Singh4 and multi-component decomposition methods for dryland crop type classification applications are evaluated, and the potential of full-polarimetric GF3 data in dryland crop type classification are also investigated. The results show that the multi-component decomposition method produces the most accurate overall classifications (88.37%). Compared with the typical polarization decomposition techniques, the accuracy of the classification results using the new decomposition method is improved. In addition, the Freeman method generally yields the third-most accurate results, and the Sato4 (87.40%) and Singh4 (87.34%) methods yield secondary results. The overall classification accuracy of the GF3 data is very positive. These results demonstrate the great promising potential of GF3 SAR data for dryland crop monitoring applications.

Skp2 regulates non-small cell lung cancer cell growth by Meg3 and miR-3163.

Maternally expressed gene 3 (Meg3) encodes a long non-coding RNA that has been shown to play a role in tumorigenesis. Skp2 is a component of the E3 ubiquitin ligase SCF that specifically promotes the ubiquitination-associated degradation of CDK inhibitor p27, and has been shown to promote cancer cell growth in different types of cancers, including non-small cell lung cancer (NSCLC). Nevertheless, a regulatory relationship between Meg3 and Skp2 has not been acknowledged. Here, we showed that NSCLC specimens had significant higher levels of Skp2 and significantly lower levels of Meg3, compared to paired non-tumor lung tissue. The levels of Meg3 and Skp2 were inversely correlated in NSCLC specimens. Patients with low Meg3 levels had a poor survival. Overexpression of Meg3 decreased Skp2 protein and increased p27 protein, while depletion of Meg3 increased Skp2 protein and decreased p27 protein in NSCLC cells, without altering Skp2 mRNA. These data suggest that the Skp2 may be regulated by Meg3 at post-transcriptional level. Bioinformatics analyses showed that miR-3163 bound to 3'-UTR of Skp2 mRNA in NSCLC cells to inhibit its translation, which was supported by luciferase reporter assay. Meg3 augmented the effects of miR-3163 on Skp2 mRNA, possibly through binding-induced function enhancement, which was supported by the double fluorescent in situ hybridization showing co-localized intracellular Meg3 and miR-3163 signals in NSCLC cells. The miR-3163 levels in NSCLC were not different from in NT, suggesting that the regulation of Skp2 in NSCLC by miR-3163 may require coordination of Meg3. Thus, our data suggest that Meg3 and miR-3163 may coordinate suppression of translation of Skp2 mRNA in NSCLC cells to inhibit NSCLC cell growth.