Grazing effects on soil properties under different soil and environmental conditions across the globe are often controversial. Therefore, it is essential to evaluate the overall magnitude and direction of the grazing effects on soils. This global meta-analysis was conducted using the mixed model method to address the overall effects of grazing intensities (heavy, moderate, and light) on 15 soil properties based on 287 papers published globally from 2007 to 2019. Our findings showed that heavy grazing significantly increased the soil BD (11.3% relative un-grazing) and PR (52.5%) and reduced SOC (-10.8%), WC (-10.8%), NO₃^- (-23.5%), and MBC (-27.9%) at 0–10 cm depth, and reduced SOC (-22.5%) and TN (-19.9%) at 10–30 cm depth. Moderate grazing significantly increased the BD (7.5%), PR (46.0%), and P (18.9%) (0–10 cm), and increased pH (4.1%) and decreased SOC (-16.4%), TN (-10.6%), and P (-23.9%) (10–30 cm). Light grazing significantly increased the SOC (10.8%) and NH₄⁺ (28.7%) (0–10 cm). Heavy grazing showed much higher mean probability (0.70) leading to overgrazing than the moderate (0.14) and light (0.10) grazing. These findings indicate that, globally, compared to un-grazing, heavy grazing significantly increased soil compaction and reduced SOC, NO₃^-, and soil moisture. Moderate grazing significantly increased soil compaction and alkalinity and reduced SOC and TN. Light grazing significantly increased SOC and NH₄⁺. Cattle grazing impacts on soil compaction, SOC, TN, and available K were higher than sheep grazing, but lower for PR. Climate significantly impacted grazing effects on SOM, TN, available P, NH₄⁺, EC, CEC, and PR. Heavy grazing can be more detrimental to soil quality based on BD, SOC, TN, C: N, WC, and K than moderate and light grazing. However, global grazing intensities did not significantly impact most of the 15 soil properties, and the grazing effects on them had insignificant changes over the years.

Summary: Studying plants using high-throughput genomics technologies is becoming routine, but interpretation of genome-wide expression data in terms of biological pathways remains a challenge, partly due to the lack of pathway databases. To create a knowledgebase for plant pathway analysis, we collected 1683 lists of differentially expressed genes from 397 gene-expression studies, which constitute a molecular signature database of various genetic and environmental perturbations of Arabidopsis. In addition, we extracted 1909 gene sets from various sources such as Gene Ontology, KEGG, AraCyc, Plant Ontology, predicted target genes of microRNAs and transcription factors, and computational gene clusters defined by meta-analysis. With this knowledgebase, we applied Gene Set Enrichment Analysis to an expression profile of cold acclimation and identified expected functional categories and pathways. Our results suggest that the AraPath database can be used to generate specific, testable hypotheses regarding plant molecular pathways from gene expression data. Availability:http://bioinformatics.sdstate.edu/arapath/ Contact:gexijin@gmail.com Supplementary Information:Supplementary data are available at Bioinformatics online.

Context: Conversion of grasslands to croplands can usually result in the degradation of soils and increased greenhouse gas (GHG) emissions such as carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). However, little is known about the impacts of grassland conversion to recently tilled croplands on soils and GHG fluxes. Aims: A field experiment was established in 2016 to evaluate the impacts of grassland conversion to tilled cropland under different landscape positions (upslope, backslope, and footslope) on select soil properties and soil GHG fluxes. Key results: The findings showed that the grassland conversion significantly increased soil bulk density and electrical conductivity but reduced pH and total nitrogen (TN). The conversion impacted soil biome community grassland and tilled croplands. The landscape position significantly impacted soil pH (footslope < upslope) and TN (footslope > upslope). The grassland conversion significantly decreased soil CO2 fluxes, but increased soil CH4 and N2O fluxes. The landscape position significantly impacted soil CO2 (footslope > upslope and backslope) and CH4 (upslope > footslope and backslope) fluxes for some periods. Soil CO2 and N2O fluxes generally followed upward and downward trends over time, respectively. Conclusions: These results indicate that grassland conversion was able to lose soil N, increase soil compaction, acidity, salts, and soil N2O and CH4 fluxes, and decrease the diversity of abundant genera and CO2 fluxes. Footslope increased TN, soil acidity, CO2, and CH4 fluxes, compared with upslope and backslope. CO2 fluxes under grassland and tilled cropland significantly increased over time, whereas N2O fluxes under grassland significantly reduced. Implications: Conversion of grassland to tilled cropland significantly impacted on sol quality. It caused a loss in soil N and increased soil compaction, acidity and salts. Grassland conversion also decreased the abundance and diversity soil microbiome.

The model plant Arabidopsis has been well-studied using high-throughput genomics technologies, which usually generate lists of differentially expressed genes under various conditions. Our group recently collected 1065 gene lists from 397 gene expression studies as a knowledgebase for pathway analysis. Here we systematically analyzed these gene lists by computing overlaps in all-vs.-all comparisons. We identified 16,261 statistically significant overlaps, represented by an undirected network in which nodes correspond to gene lists and edges indicate significant overlaps. The network highlights the correlation across the gene expression signatures of the diverse biological processes. We also partitioned the main network into 20 sub-networks, representing groups of highly similar expression signatures. These are common sets of genes that were co-regulated under different treatments or conditions and are often related to specific biological themes. Overall, our result suggests that diverse gene expression signatures are highly interconnected in a modular fashion.

Soil carbon dioxide (CO₂) emissions from the field of corn (Zea maysL.) play an important role in global warming. This study investigated temporal variability of soil CO₂fluxes (R_s) with soil temperature (T_s) and moisture (θ) and built DAYCENT models for predicting future impacts of climate changes onR_susing the measured high-frequency data.R_strend was tested by Mann–Kendall and Sen Estimator. PredictedR_ss under different climate scenarios were compared using Parallel-line Analysis. The findings indicated that dailyR_sexponentially increased withT_sconstrained byθ. During theθof 27–31%, there was a strong exponential relationship betweenR_sandT_s, but the relationship was weaker for theθof 38–41% and 22–26%. Soil environmental index (SEI,T_s×θ) significantly impactedR_swith linear regressionR_s^0.5= 0.4599 + 0.002059 × SEI in 2008, 2009 and 2011. At the diurnal scale, there were different trends inR_ss and relationships amongR_sandT_sandθin different years. Predicted yearlyR_ss, rootR_ss and corn yield in 2014–2049 increased with an increase in temperature scenarios, but theR_ss significantly increased as temperature rose by 1°C or higher. Predicted yearlyR_ss, rootR_ss and yield reduced with precipitation scenario increase, and the rootR_ss and yield significantly diminished as precipitation increased by 15 and 30%. Predicted yearlyR_sfrom cornfields had a significantly increasing trend. Future research is needed to explore methods for mitigating cornfieldR_sand evaluating sensitivities of different croplandR_ss to temperature changes.