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Soil Importation and Wadi Channel Narrowing for Agricultural Expansion Alter Sediment Patterns and Soil Chemistry in Arid Oman
No full textWadis—ephemeral watercourses in arid regions—serve as vital ecohydrological corridors, supporting both biodiversity and water resource functions. Yet, these fragile systems are increasingly threatened by human interventions. Impacts of agricultural expansion, particularly soil importation and wadi occlusion, on sediment dynamics and surface-soil properties in Wadi Al-Khoud, Oman are investigated. Sampling at three transects across the wadi (n = 48), combined with laboratory analysis, is done. Spatial statistics was used to assess soil texture, topsoil chemistry, and downstream nabkhas as geomorphic landforms. The imported “alien” soils—often mixed with waste like construction debris, crashed rock from local quarries and stripped asphalt—altered soil composition, with concentrations of Na+, NO3;−, and K+ increased by 249.11%, 32.71% and 103.93%, respectively, compared to the control. Texture at the anthropogenically impacted site showed a significant increase in fine silt content by 4.28 folds compared to control. Semivariogram analysis corroborated disrupted spatial texture patterns. Over a decade of 2010–2020, wadi's average width contracted by 73 m, amounting to approximately 95,838 m3 of imported material. This narrowing induced a Venturi contracta effect, which increases the flow velocities, and consequently scoures more sediments, thereby intensifying flood-driven erosion and contaminant dispersal. The formed nabkhas reflect ecological shifts in the wadi/floodplain. Nabkhas buffer sediment loss but alarm the instability of the upstream sediments. Legal frameworks akin to urban landscaping regulations are urgently needed to protect wadis as “natural hydraulic veins” and critical environmental assets in Oman's rapidly urbanizing drylands
Farmland trees and integrated pest management: A review of current knowledge and developing strategies for sustainable systems
No full text1. Climate change and the withdrawal of several classes of agrochemicals from useare intensifying the challenges faced by food producers in controlling pests incrop systems. Integrated pest management (IPM), which uses a combination ofpest control approaches, is therefore a focus in international initiatives to im-prove the resilience of food production.2. Integrating the greater use of trees and shrubs on farms within IPM frameworksoffers a biodiversity-positive contribution to crop protection. For example, treescan modulate the prevalence and impacts of agricultural pests and their natu-ral antagonists through direct and indirect interactions. The beneficial impact offarmland trees and shrubs on pest management in arable or grassland fields canbe enhanced from an analysis of variables such as tree species and their spatialdistribution on farms, insect-plant dynamics, population behaviours and soil man-agement practices.3. The aim of this study is to synthesise existing knowledge and to assess the ben-efits and trade-offs between farmland trees and IPM strategies, building on gapsin knowledge identified by a stakeholder survey. Through this targeted review,we delineate the future evidence required to define and quantify the advantagesthat farmland trees offer as an element of IPM strategies.4. Practical implication. The development of regional biodiversity monitoring tools,which integrate landscape features such as trees, shows promise for shapingnational policies to increase the adoption of IPM. There is a demand for user-friendly on-farm tools, adaptable to changing crop and pest priorities, that cansupport the alignment of the management of farm trees with IPM. However, basic and applied biological and ecological research are needed to inform and validatethese decision-support tools and the capability to inform landscape-scale models
Agricultural practices can threaten soil resilience through changing feedback loops
No full textSoil has supported terrestrial food production for millennia; however, agricultural intensification may affect its resilience. Using a systems-thinking-approach we reviewed the impacts of conventional-agriculture practices on soil resilience and identified alternative practices that could mitigate these effects. We found that many practices only affect soil resilience with their long-term repeated use. Lastly, we ranked the impacts which pose the greatest threats to soil resilience and, consequently, food and feed security
Exploring the relationship between physical properties and sensory characteristics of newly developed white breads with improved nutritional composition – initial insights
No full textStaple foods such as bread are key contributors to the diet. Accordingly, there is growing emphasis on improving the nutritional composition of wheat (e.g., increasing the dietary fibre content); however, the impacts of such improvements on bread characteristics remains unclear. A series of experiments were conducted to determine the relationship between physical properties (slice dimensions, cell crumb, water activity, moisture content, colour and texture analysis) and sensory profile (via a trained sensory panel; n = 12) of five newly developed white breads compared with a commercial standard. Overall, sensory profiling identified twenty-seven attributes to describe the breads; key differences between breads related to the appearance (i) colour: crust (top/side) and crumb (centre) and (ii) density which could be explained by physical properties to varying extents. For example, breads higher in dietary fibre tended to have smaller slice height, larger cell area, higher water activity and moisture content as well as instrumental texture (springiness) and colour (darker) differences. In summary, findings are promising in terms of tested white bread prototypes and provide key insights for further product development. Going forwards, developing nutritionally enhanced white bread without modulating cost and quality could have noteworthy public health benefits
Evaluating boundary line fitting approaches for detecting yield-limiting factors and critical soil nutrient concentrations
No full textClosing the crop yield gap is critical to meeting rising global food demand driven by population growth. The boundary line (BL) methodology is widely used to assess yield gaps and identify its causes. However, the lack of a standard BL fitting method can lead to inconsistencies in outputs and recommendations. This study compared four BL fitting methods, binning, BOLIDES, quantile regression (QR), and the censored bivariate normal model (cbvn), in determining the most-limiting factor and critical values (\u1d465crit) across three datasets from England (Dataset 1), East Africa (Dataset 2), and a nutrient omission-trial from Ethiopia (Dataset 3). The most-limiting factor was identified using the Law of the Minimum and experimentally via omission-trials. Agreement among BL fitting methods and between BL methodology and omission-trials was tested using Cohen/Fleiss \u1d705-statistic. The consistency of \u1d465crit from BL fitting methods was assessed using the 95% confidence interval (CI) of cbvn and compared to RB209 guidelines (Dataset 1 only). Additionally, stakeholder preferences/opinions on BL fitting methods were gathered via workshops in Nairobi and Harare. Results showed BL fitting methods generally identified the most-limiting factor consistently (\u1d705 > 0.4), but inconsistencies were observed for binning and QR methods. Experimentally-determined most-limiting factors were inconsistent with BL outputs (\u1d705 < 0.2). While most \u1d465crit estimates fell within the cbvn CI, deviations occurred, especially in Dataset 2. BL fitting methods often underestimated \u1d465crit compared to RB209 guidelines. Stakeholder exercise showed no evidence (p = 0.56) against the null hypothesis of uniform ranking of BL fitting methods. The study highlights that while BL fitting methods show general consistency, discrepancies with experimentally determined results exist. Despite consistent results, cbvn is recommended for critical nutrient estimation due to its uncertainty quantification, supporting probabilistic insights for agronomic decisions
Improved gene annotation of the fungal wheat pathogen Zymoseptoria tritici based on combined Iso-Seq and RNA-Seq evidence
No full textDespite large omics datasets, the establishment of a reliable gene annotation is still challenging for eukaryotic genomes. Here, we used the reference genome of the major fungal wheat pathogen Zymoseptoria tritici (isolate IPO323) as a case study to develop methods to improve eukaryotic gene prediction. Four previous IPO323 annotations identified 10,933 to 13,260 gene models, but only one third of these coding sequences (CDS) have identical structures. To resolve these discrepancies and improve gene models, we generated full-length transcripts using long-read sequencing. This dataset was used together with other evidence (RNA-Seq transcripts and protein sequences) to generate novel ab initio gene models. The selection of the best structure among novel and existing gene models was performed according to transcript and protein evidence using InGenAnnot, a novel bioinformatics suite. Overall, 13,414 re-annotated gene models (RGMs) were predicted, including 671 new genes among which 53 encoded effector candidates. This process corrected many of the errors (15%) observed in previous gene models (coding sequence fusions, false introns, missing exons). While fungal genomes have poor annotations of untranslated regions (UTRs), our Iso-Seq long-read sequences outlined 5’ and 3’UTRs for 73% of the RGMs. Alternative transcripts were identified for 13% of RGMs, mostly due to intron retention (75%), likely corresponding to unprocessed pre-mRNAs. A total of 353 genes displayed alternative transcripts with combinations of previously predicted or novel exons. Long non-coding transcripts (lncRNAs) and double-stranded RNAs from two fungal viruses were also identified. Most lncRNAs corresponded to antisense transcripts of genes (52%). lncRNAs that were up or down regulated during infection were enriched in antisense transcripts (70%), suggesting their involvement in the control of gene expression. Our results showed that combining different ab initio gene predictions and evidence-driven curation using InGenAnnot improved the quality of gene annotations of a compact eukaryotic genome. Our analysis also provided new insights into the transcriptional landscape of Z. tritici, helping develop an increasingly complex picture of its biology
Sensitivities in associating land-system archetypes with sustainability metrics Insights from simulations
Full text linkArchetypes of land- and socio-ecological systems, generated using unsupervised classification methods, enable the assimilation of complex environmental and socio-economic information. Such simplification has considerable potential to feed into decision support systems for sustainability planning. But, the usefulness of archetypes depends on how well they relate to sustainability criteria, such as ecosystem service (ES) delivery, that are external to the input datasets employed for archetype generation. Sensitivities in such post-hoc association analyses, and the associated utility of the archetype framework in a decision support context, remain unexplored. Here we emulated post-hoc association analysis procedures using simulated socio-ecological datasets and ES response variables. Our simulations revealed a substantial influence on analysis performance from (1) the number of variables used as inputs in archetype generation, (2) the correlation structure of input datasets, (3) the type and distribution of input variables, and (4) the functional form (linear or non-linear) characterising the relationship between ES variables and their predictors. We observed near-identical performance when archetypes were generated using K-means clustering and Self-Organising Maps (SOMs) – two commonly used archetype classification methods. Further, better archetype classifier performance did not guarantee better discrimination of ES value distributions between archetypes. Our results suggest that designing a framework to generate archetypes for sustainability planning, and the selected methodological choices therein, should place greater emphasis on what the archetypes will be used for in downstream analyses, and not focus solely on archetype classifier performance. This would better ensure the identification of archetypes adaptable to a diverse array of sustainability indicators and sufficiently robust for monitoring decision outcomes over time
Spatial distribution and drivers of soil organic carbon content in croplands of Morocco
No full textSoil organic carbon (SOC), a critical component of soil organic matter, plays a vital role in enhancing soil productivity, ensuring soil stability, and mitigating CO2 emissions. Climate, mineralogy, and vegetation-derived factors may each contribute to the cycling of SOC, but whether its distribution varies predictably in Mediterranean arid croplands remains ambiguous. In a spatiotemporal machine learning framework, a multi-year dataset of topsoil organic carbon concentrations from over 31,000 cropland sites in Morocco were matched with corresponding environmental covariates including climate, vegetation, topography, and soil properties. The spatiotemporal dataset was used for model training and cross-validation, while model extrapolations estimated SOC spatiotemporal changes between 2000 and 2020 at a 250 m ground resolution. The aim was to assess the environmental drivers influencing spatiotemporal changes of SOC concentrations in these croplands. Measured topsoil SOC concentrations showed low median 11.71 g C kg-1 with high variability across the studied soils (Q1 = 8.46 and Q3 = 16.24 g C kg-1). Fifty-seven percent of the variance in SOC content was explained by a suite of bioclimatic proxies related to temperature, vegetation, and precipitation, with temperature seasonality and annual mean temperature having the highest impact on carbon concentrations. Collectively, the national carbon dataset supports a new basis for understanding the local drivers of SOC gains and losses in arid croplands of Morocco. This will partly address controversy concerning carbon cycling in arid soils and responses to climate change
Transcriptomics-Driven Discovery of New Meroterpenoid Rhynchospenes Involved in the Virulence of the Barley Pathogen Rhynchosporium commune
No full textRhynchosporium commune, the causal agent of barley scald disease, poses a major threat to global barley production. Despite its significant impact, the molecular mechanisms underlying R. commune’s infection process remain largely unexplored. To address this, we analyzed the differential gene expression data of R. commune WAI453 cultivated under both in planta and in vitro conditions, aiming to identify secondary metabolite biosynthetic gene clusters that are potentially involved in the pathogenicity of R. commune. Our analysis revealed increased expression of a polyketide-terpene gene cluster (the rhy cluster), containing a specific myeloblastosis (MYB)-type transcription factor gene rhyM, during in planta growth. Overexpression of rhyM in an axenic culture activated the expression of the rhy cluster, resulting in the production of a series of new meroterpenoid metabolites, which we named rhynchospenes A–E. Their structures were elucidated through a combination of spectroscopic methods and single crystal X-ray diffraction analysis. Infiltration of rhynchospenes into barley leaves resulted in strong necrosis, with rhynchospene B demonstrating the highest phytotoxicity and causing necrosis at a minimum concentration of 50 ppm. Silencing rhyM in R. commune WAI453 confirmed the role of rhynchospenes as virulence factors in barley disease. The resulting mutant showed significantly reduced expression of the rhy cluster in planta compared to the wild-type strain and decreased virulence in seedling pathogenicity assays on barley. The characterization of the rhy cluster and rhynchospenes provided insights into the role of secondary metabolites in R. commune virulence and barley scald disease development. The study also highlights the potential us
Three Years After Soybean-Cover-Crop Rotation in Conventional and No-Till Practices: What Are the Consequences on Soil Nitrous Oxide Emissions?
No full textNitrous oxide is a potent greenhouse gas due to its long atmospheric lifespan (121 years) that results in a high global warming potential (GWP). Research has shown that no-tillage may be implemented as a mitigation strategy to reduce N2O emissions. The objective of the was to evaluate how conventional tillage (CT) and no-tillage (NT) can potential influence N2O emissions in soybean rotation in a semi-arid region of the central Free State of South Africa. The effect of conventional and no-till tillage practices on N2O emissions under soybean rotation was evaluated in the 3rd year of a 5-year rotation system, in a semi-arid region of the Free State of South Africa, from December 2022 to December 2023. The experimental area was divided into three blocks and there were two plots in each block: in total there were six plots. The treatments were planted in a soybean rotation system under no-tillage and conventional tillage. The monthly averages of N2O emissions were significantly different from each other during the soybean growing season; the highest emissions were recorded in August/September 2023 from both the NT and CT treatments after harvest. During this time, there were crop residues in the soil that increased soil carbon. There was a positive correlation between N2O emissions and soil carbon content (p = 0.21) and between N2O emissions and soil organic matter (p = 0.43). Emissions were significantly higher in CT (LSD = 0.3) than in NT. The lowest N2O emissions were recorded in December 2023 (LSD = 0.05) and were significantly reduced in the no-till plots compared to those of the conventional tillage plots. Furthermore, the lowest cumulative N2O emissions of 0.26 ± 0.22 kg N2O-N ha−1 were recorded during NT in the winter season and were significantly different from CT (LSD = 0.19). The results from our study indicate that the no-till practices in soybean rotation can decrease N2O emissions