Zinc is an essential minor element for rice growth and human health, which can also change the structure of the microorganisms. However, it remains unclear for the effects of zinc fertilizer on microbiome function in agricultural soils and crops. To solve this research gap, we investigated the relationship between improving rice (Oryza sativa L.) yield, Zn concentration, soil microbial community diversity, and function by the application of Zn fertilizer. The field trials included three rice varieties (Huanghuazhan, Nanjing9108, and Nuodao-9925) and two soil Zn levels (0 and 30 kg ha–1) in Jiangsu province, China. As a test, we studied the variety of soil bacterial composition, diversity, and function using 16S rRNA gene sequencing. The results showed that soil Zn application reduced the diversity of microbial community, but the bacterial network was more closely linked, and the metabolic function of bacterial community was improved, which increased the grain yield (17.34–19.52%) and enriched the Zn content of polished rice (1.40–20.05%). Specifically, redundancy analysis (RDA) and Mantel’s test results revealed soil total nitrogen (TN) was the primary driver that led to a community shift in the rice rhizosphere bacterial community, and soil organic carbon (SOC) was considered to have a strong influence on dominant phyla. Furthermore, network analysis indicated the most critical bacterial taxa were identified as Actinobacteria, Bacteroidetes, Proteobacteria, and Chloroflexi based on their topological roles of microorganisms. KEGG metabolic pathway prediction demonstrated that soil Zn application significantly (p < 0.05) improved lipid metabolism, amino acid metabolism, carbohydrate metabolism, and xenobiotic biodegradation. Overall, their positive effects were different among rice varieties, of which Nanjing-9108 (NJ9108) performed better. This study opens new avenues to deeply understand the plant and soil–microbe interactions by the application of fertilizer and further navigates the development of Zn-rich rice cultivation strategies.
Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).
To determine the effects of air pollution on crop yields, weather, air pollution, and maize and winter wheat yield data from 331 cities in China from 2014 to 2016 were collected and analysed. Furthermore, support vector regression and the crop growth model were applied to extrapolate the air pollution data of Beijing and Hetian and verify the relationship between air pollution and yield. Precisely, heavy air pollution usually occurred in North China, but less than moderate air pollution levels affected crop yields statistically insignificantly. Moreover, both the winter wheat and maize yields increased in moderate air pollution periods but decreased in heavy air pollution periods in 2014, 2015 and 2016. Importantly, a threshold value was necessary for the heavy air pollution periods to trigger a yield decrease. The threshold values of maize in 2015 and 2016 were 7 days and 5 days, respectively, while that of winter wheat was 10 days in both 2015 and 2016. Once the heavy air pollution periods exceeded the threshold value, both the winter wheat and maize yields decreased linearly with the periods. PM2.5 was the main air pollutant in Beijing in 2014, while PM2.5 and PM10 were the main air pollutants in Hetian in both 2015 and 2016. Regardless of whether the main air pollutant was PM2.5 or PM10, the simulated potential winter wheat yields by the crop growth model with moderate air pollution for the whole growth period were all higher than the yields under observed and heavy air pollution conditions.
<p>Meteorological disasters such as windstorm, waterlogging, drought and so on, are crucial factors affecting crop production and farmers&#8217; income. Agricultural insurance is one of the important strategies to protect the interests of farmers, especially in developing countries such as China. However, the accurate identification and quantification of meteorological disasters in large scale are still difficult issues for the popularization and development of agricultural insurance. One possible solution is to combine the high-resolution remote sensing satellite images with machine learning algorithms. In this study, we conducted the measurements for the yield of soybean and maize and determined the damage degrees of about 2000 fields in 2021. The Sentinel-2 satellite images were also collected in the same or adjacent date as the field measurements. The clustering algorithm was applied to amplify the field measurements. After that, three machine learning algorithms named LightGBM, XGboost and RandomForest were used to relate the surface reflectance, crop types, disaster damage degrees, and crop yields of soybean and maize. The results indicated that the accuracy of the XGBoost algorithm is better than the LightGBM and RandomForest. In addition, the present method obtained higher accuracy for the maize than the soybean, which indicates that meteorological and image data during crop growth periods should also be added in the yield estimation process, and the differences between crop loss mechanisms of different crops should be studied in the future.</p>
<p>Soil-root hydraulic resistance variation and stomatal regulation are two critical hydrophysiological responses of plants to drought stress; however, few studies have been developed to quantify their interactions. To fill this gap, we developed a soil-plant hydraulic model (SR-HRV) that attempts to characterize the effects of stomatal regulation and three universal soil-root hydraulic resistance variations, i.e., root aquaporins promotion (AQU), apoplastic path damage (APD), and root-soil contact loosening (CONTACT). The sensitive parameters of the SR-HRV model were analyzed and optimized based on a field experiment with sunflower plants (<em>Helianthus annuus</em> L.). Several simulation scenarios were designed to clarify the individual and interactive effects of soil-root hydraulic resistance variations for plants with different stomatal sensitivities. Results show that the sensitivity of simulated stomatal conductance and soil water content response to stomatal regulation parameters, especially to abscisic acid-related parameters, are more active than to soil-root hydraulic resistance variation parameters. But as the soil dries, the sensitivities to APD and CONTACT parameters are rapidly increased. The simulation demonstrates that AQP alleviates the leaf water potential drop-down and maintains relatively high root water absorption of the plant when it is in mild drought conditions, while CONTACT and APD respectively restrict the water flux and drought signal responses with continuous soil dehydration. Moreover, the AQP effects are more pronounced but the effects of APD and CONTACT would be restricted for plants with higher stomatal sensitivity to drought signals. These simulation results imply the diverse response strategies of plants to drought, the collaborations between stomatal regulation and soil-root hydraulic resistance variations should be considered in soil-plant water transport modeling.</p><p>&#160;</p>
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