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Colletotrichum species are the most common cause of maize anthracnose, which often manifests as leaf spots. However, field observations often reveal symptoms similar to those caused by other leaf spot pathogens, such as Curvularia and Bipolaris. In this study, 99 isolates were identified using tissue separation and single-spore isolation techniques. As preliminary measures of species diversity, all 99 isolates were identified morphologically, and the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene sequences were phylogenetically analyzed. Furthermore, 48 representative strains were selected for molecular identification using multi-locus phylogenetic analyses based on five gene loci (ITS, TUB, ACT, GAPDH, and CAL). Finally, 10 species of Colletotrichum isolated from maize leaf spots were identified. Colletotrichum cliviicola was the most dominant species (24.2%), followed by C. fructicola (18.2%), C. karstii (16.1%), C. siamense (13.1%), C. boninense (7.1%), C. kahawae (7.1%), C. brevisporum (6.1%), C. truncatum (5.1%), C. gigasporum (2.0%), and C. gloeosporioides (1.0%). For the first time, pathogenicity tests revealed that C. cliviicola, C. fructicola, C. siamense, C. karstii, and C. truncatum are the causative agents of maize anthracnose. Additionally, C. boninense was identified as an endophytic fungus on healthy maize. In conclusion, this study is the first to identify the pathogen of maize anthracnose in Sichuan Province. It provides valuable insights for accurately diagnosing and managing maize anthracnose.

Ligusticum chuanxiong Hort. is considered an important medicinal herb with extremely high economic value and medicinal value due to its various effects, including anti-oxidation, sedative action, hepatoprotection, and invigorating blood circulation. However, L. chuanxiong cultivation is hampered by various plant diseases, especially the root rot caused by Fusarium solani, hindering the sustainable development of the L. chuanxiong industry. The occurrence of soil-borne diseases is closely linked to imbalances in the microbial community structure. Here, we studied the yields, rhizosphere microbiota, and soil physiochemical characteristics of healthy and diseased L. chuanxiong plants affected by root rot with high-throughput sequencing and microbial network analysis, aiming to explore the relationships between soil environmental factors, microbiomes, and plant health of L. chuanxiong. According to the results, L. chuanxiong root rot significantly decreased the yields, altered microbial community diversity and composition, enriched more pathogenic fungi, recruited some beneficial bacteria, and reduced microbial interaction network stability. The Mantel test showed that soil organic matter and pH were the major environmental factors modulating plant microbiome assembly. The root rot severity was significantly affected by soil physiochemical properties, including organic matter, cation exchange capacity, available nitrogen, phosphorus, potassium, and pH. Furthermore, two differential microbes that have great potential in the biocontrol of L. chuanxiong root rot were dug out in the obtained results, which were the genera Trichoderma and Bacillus. This study provided a theoretical basis for further studies revealing the microecological mechanism of L. chuanxiong root rot and the ecological prevention and control of L. chuanxiong root rot from a microbial ecology perspective.

Clubroot is a major disease and severe threat to Chinese cabbage, and it is caused by the pathogen Plasmodiophora brassicae Woron. This pathogen is an obligate biotrophic protist and can persist in soil in the form of resting spores for more than 18 years, which can easily be transmitted through a number of agents, resulting in significant economic losses to global Chinese cabbage production. Rhizosphere microbiomes play fundamental roles in the occurrence and development of plant diseases. The changes in the rhizosphere microorganisms could reveal the severity of plant diseases and provide the basis for their control. Here, we studied the rhizosphere microbiota after clubroot disease infections with different severities by employing metagenomic sequencing, with the aim of exploring the relationships between plant health, rhizosphere microbial communities, and soil environments; then, we identified potential biomarker microbes of clubroot disease. The results showed that clubroot disease severity significantly affected the microbial community composition and structure of the rhizosphere soil, and microbial functions were also dramatically influenced by it. Four different microbes that had great potential in the biocontrol of clubroot disease were identified from the obtained results; they were the genera Pseudomonas, Gemmatimonas, Sphingomonas, and Nocardioides. Soil pH, organic matter contents, total nitrogen, and cation exchange capacity were the major environmental factors modulating plant microbiome assembly. In addition, microbial environmental information processing was extremely strengthened when the plant was subjected to pathogen invasion, but weakened when the disease became serious. In particular, oxidative phosphorylation and glycerol-1-phosphatase might have critical functions in enhancing Chinese cabbage’s resistance to clubroot disease. This work revealed the interactions and potential mechanisms among Chinese cabbage, soil environmental factors, clubroot disease, and microbial community structure and functions, which may provide a novel foundation for further studies using microbiological or metabolic methods to develop disease-resistant cultivation technologies.

South Asia, one of the most important food producing regions in the world, is facing a significant threat to food grain production under the influence of extreme high temperatures. Furthermore, the probability of simultaneous trends in extreme precipitation patterns and extreme heat conditions, which can have compounding effects on crops, is a likelihood in South Asia. In this study, we found complex relationships between extreme heat and precipitation patterns, as well as compound effects on major crops (rice and wheat) in South Asia. We also employed event coincidence analysis (ECA) to quantify the likelihood of simultaneous temperature and crop extremes. We used the Enhanced Vegetation Index (EVI) as the primary data to evaluate the distinct responses of major crops to weather extremes. Our results suggest that while the probability of simultaneous extreme events is small, most regions of South Asia (more than half) have experienced extreme events. The regulatory effect of precipitation on heat stress is very unevenly distributed in South Asia. The harm caused by a wet year at high temperature is far greater than that during a dry year, although the probability of a dry year is greater than that of a wet year. For the growing seasons, the highest significant event coincidence rates at a low EVI were found for both high- and low-temperature extremes. The regions that responded positively to EVI at extreme temperatures were mainly concentrated in irrigated farmland, and the regions that responded negatively to EVI at extreme temperatures were mostly in the mountains and other high-altitude regions. Implications can guide crop adaptation interventions in response to these climate influences.

A non-iterative method is presented to accurately extract the five parameters of single diode model of solar cells in this paper. This method overcomes the problems of complexity and accuracy by simplifying the calculation process. Key parts of the equation are to be adjusted dynamically so that the desired five parameters can be obtained from the I-V curve. Then, the I-V and P-V characteristic curves of solar cells are used to compare the effectiveness of this method with other methods. Furthermore, the root mean square error analysis shows that this method is more applicable than other methods. Finally, the I-V and P-V characteristics simulated by using the extracted parameters in this method are compared and discussed with the experimental data of solar cells under different conditions. In fact, this extraction process can be regarded as an effective and accurate method to estimate solar cells’ single diode model parameters.

Grain self-sufficiency is a national food security target of China. The way that built-up land expansion impacts upon cropland loss and food provision needs to be explored in the major grain producing areas. Shandong Province is an important agricultural food production region, which is also experiencing rapidly urbanizing. Here we assessed the spatiotemporal distribution of cropland loss due to built-up land expansion and landscape dynamics of cropland during 2000–2020, by using 30 m resolution land cover data. We also analyzed the potential yield change influenced by cropland loss. The results showed that the area of built-up land expanded by 5199 km2 from 2000–2010, and 11,949 km2 from 2010–2020. Approximately 95% of the new built-up land was from cropland during the two stages, and the primary mode of built-up land expansion was the edge expansion. The patch density and the patch size of cropland kept increasing and decreasing, respectively, and the aggregation index kept decreasing from 2000 to 2020, indicating increased cropland fragmentation. The proportion of occupied cropland with potential yield greater than 7500 kg/ha was 25% and 37% during the former and the latter period. Thus, higher quality cropland was encroached in the recent period. The findings could provide meaningful implications for making sustainable land use development strategies in the study area and other similar regions.

Land use and land cover (LULC) change influences many issues such as the climate, ecological environment, and economy. In this study, the LULC transitions in the Yellow River Basin (YRB) were analyzed based on the GlobeLand30 land use data in 2000, 2010, and 2020. The intensity analysis method with hypothetical errors calculation was used, which could explain the deviations from uniform land changes. The strength of the evidence for the deviation was revealed even though the confusion matrixes of the LULC data at each time point for the YRB were unavailable. The results showed that at the interval scale, the land transition rate increased from the first to the second period for all of the upper, middle, and lower reaches. The exchange component was larger than the quantity and shift component, and the gross change was 4.1 times larger than the net change. The size of cultivated land decreased during both intervals. The artificial surfaces gains were active for all three reaches and had strong evidence. A hypothetical error in 93% of the 2000 data and 58% of the 2010 data can explain deviations from uniform transition given woodland gain during 2000–2010 and 2010–2020. Ecological restoration projects such as Grain for Green implemented in 2000 in the upper reaches resulted in the woodland increase.

As a newly emerging satellite form of data, solar-induced chlorophyll fluorescence (SIF) provides a direct measurement of photosynthetic activity. The potential of SIF for drought assessment in different grassland ecosystems is not yet clear. In this study, the correlations between spaceborne SIF and nine drought indices were evaluated. Standardized precipitation evapotranspiration index (SPEI) at a 1, 3, 6, 9, 12 month scale, Palmer drought severity index (PDSI), soil moisture, temperature condition index (TCI), and vapor pressure deficit (VPD) were evaluated. The relationships between different grassland types and different seasons were compared, and the driving forces affecting the sensitivity of SIF to drought were explored. We found that the correlations between SIF and drought indices were different for temperate grasslands and alpine grasslands. The correlation coefficients between SIF and soil moisture were the highest (the mean value was 0.72 for temperate grasslands and 0.69 for alpine grasslands), followed by SPEI and PDSI at a three month scale, and the correlation coefficient between SIF and TCI was the lowest (the mean value was 0.38 for both temperate and alpine grasslands). Spaceborne SIF is more effective for drought monitoring during the peak period of the growing season (July and August). Temperature and radiation are important factors affecting the sensitivity of SIF to drought. The results from this study demonstrated the importance of SIF in drought monitoring especially for temperate grasslands in the peak growing season.

Anthracnose caused by Colletotrichum spp. is an important disease of blueberries and results in large economic losses for blueberry growers. Samples of anthracnose were collected from six main blueberry cultivation areas in Sichuan Province. In total, 74 Colletotrichum isolates were obtained through a single-spore purification method and identified to the species through morphological characteristics and phylogenetic analyses based on partial DNA sequences of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), internal transcribed spacer (ITS) regions, and the β-tubulin (TUB2), actin (ACT) and calmodulin (CAL) genes. Among all species, Colletotrichum fructicola was the most dominant species, with an isolation percentage of up to 66.2% in Sichuan, followed by Colletotrichum siamense (17.6%), C. kahawae (5.4%), C. karstii (5.4%), C. nymphaeae (2.7%) and C. sichuaninese (2.7%). Pathogenicity tests showed all species were able to cause typical anthracnose symptoms on blueberry leaves and stems. Colletotrichum fructicola was the predominant species with strong aggressiveness. Moreover, C. fructicola, C. kahawae, C. sichuaninese and C. nymphaeae are first reported here to cause blueberry anthracnose. This study provides a comprehensive reference for the association of different Colletotrichum spp., which may support the sustainable management of blueberry anthracnose.

Many species of the genus Bipolaris are important plant pathogens and often cause leaf spot, root rot, and seedling blight in an extremely wide range of hosts around the world. In recent years, maize leaf spot caused by Bipolaris species has frequently occurred with complex symptoms and is becoming increasingly serious in Sichuan Province of China. To investigate the population diversity of Bipolaris spp. and their corresponding symptoms in maize, 747 samples of maize leaf spot were collected from 132 sampling sites in 19 administrative districts of Sichuan Province from 2011 to 2018. Based on morphological characteristics, pathogenicity testing, and phylogenetic analysis of the rDNA internal transcribed spacer (ITS) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) genes, a total of 1186 Bipolaris isolates were identified as B. maydis, B. zeicola, B. cynodontis, B. oryzae, B. setariae, and B. saccharicola, among which B. maydis and B. zeicola were the dominant pathogenic species, accounting for 57.34% and 42.07% of the isolates, respectively. We found that B. zeicola isolates were mainly distributed in high altitude and cool mountainous areas, while B. maydis was more widely distributed in Sichuan Province. The typical symptoms caused by the Bipolaris species were clearly distinct in maize. The typical symptoms caused by B. maydis were elongated strip lesions, or fusiform, elliptical lesions, and those caused by B. zeicola were narrow linear lesions. Herein, B. saccharicola was first reported on maize and caused subrotund lesions. This study provides useful information for disease diagnosis and management for Bipolaris leaf spot in maize.

Accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. Forest AGB estimation has been conducted with a variety of data sources and prediction methods, but many uncertainties still exist. In this study, six prediction methods, including Gaussian processes, stepwise linear regression, nonlinear regression using a logistic model, partial least squares regression, random forest, and support vector machines were used to estimate forest AGB in Jiangxi Province, China, by combining Geoscience Laser Altimeter System (GLAS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, and field measurements. We compared the effect of three factors (prediction methods, sample sizes of field measurements, and cross-validation settings) on the predictive quality of the methods. The results showed that the prediction methods had the most considerable effect on the prediction quality. In most cases, random forest produced more accurate estimates than the other methods. The sample sizes had an obvious effect on accuracy, especially for the random forest model. The accuracy increased with increasing sample sizes. The random forest algorithm with a large number of field measurements, was the most precise (coefficient of determination (R2) = 0.73, root mean square error (RMSE) = 23.58 Mg/ha). Increasing the number of folds within the cross-validation settings improved the R2 values. However, no apparent change occurred in RMSE for different numbers of folds. Finally, the wall-to-wall forest AGB map over the study area was generated using the random forest model.

The stress-absorption layer in cement concrete pavement delays the development of reflection cracks and is good at fatigue resistance. Laboratory investigations of the anti-crack performance of the high viscous asphalt sand stress-absorption layer (HVASAL) and rubber asphalt stress-absorption layer (RASAL) were carried out by force-controlled fatigue crack propagation tests, for which three types of overlay structures with three types of pre-crack (i.e., the middle crack, the side crack, and the 45° inclined crack) were designed. A probability model was established to describe the propagation of the fatigue cracks. The fatigue crack propagation, the fatigue life, the crack propagation rate, and the crack propagation mechanism of the three types of overlay structure were compared and analyzed. The results show that the stress-absorption layers have good anti-crack fatigue performance, and that the RASAL is better than the HVASAL. The crack propagation patterns of the three types of overlay structure were found. In the double logarithmic coordinate, the curves of the three types of cracks are straight lines with different intercepts and slopes. The probability model quantifies the relationship between the crack propagation rate and ∆K. The influences of the three types of crack on the fatigue properties of the asphalt overlays are different.