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9 results for “impacts of climate change on agriculture”
Updated Supplementary Figures for Can leafhoppers help us trace the impact of climate change on agriculture?
<p>Supplementary Figures for: <strong>Can</strong> <strong>leafhoppers help us trace the impact of climate change on agriculture? </strong>to be posted in bioRxiv.</p><p><strong>Figure S1. </strong>Diversity indexes calculated in this study to compare leafhopper diversity each growing season investigated in this study and the geographic regions where the strawberry fields were located. Statistical analyses were performed for Shannon and Simpson finding that in both cases there is no interaction between years and regions with <i>p</i> = 0.0889 and <i>p</i> = 0.7139, respectively.</p><p><strong>Figure S2.</strong> Distinctive RFLP patterns obtained with <i>Cpn</i>ClassiPhyR from <i>in silico</i> digestion of <i>cpn60</i>UT from SbGPQ clones and AY-Col. Lanes labelled MW in <i>in silico</i> RFLP represent <i>Hae</i>III-digested phage <i>ϕ</i>X174 DNA.</p><p><strong>Figure S3.</strong> Phylogenetic tree using neighbour-joining method of the <i>16S, secY, nusA, rp, secA, cpn60 </i>and<i> tuf</i> sequences obtained in this study for the SbGP phytoplasma and sequences retrieved from Genbank. <i>Acholeplasma laidlawii</i> PG8 was used as an outgroup. The phylogenetic tree was bootstrapped 1000 times to achieve reliability. Bar, 1 substitution in 100 or 500 positions. </p><p><strong>Fig. S3 Panel 1: </strong>cpn60UT, tuf, and secY trees.</p><p><strong>Fig. S3 Panel 2:</strong> nusA, rp, and secA trees.</p><p><strong>Fig. S3 Panel 3:</strong> 16S tree with subtree showing heterogeneity of SbGPQ and 'Ca. P. tritici'.</p><p><strong>Figure S4.</strong> Leafhopper feeding-associated damages observed in strawberry plants. <strong>A</strong>, in the field. <strong>B</strong>, in the greenhouse after incubation with leafhoppers.</p><p><strong>Figure S5.</strong> Alpha diversity indexes were calculated to study <i>Macrosteles quadrilineatus</i> microbiome observed for each growing season. No statistical difference was observed among the sites for any of the indexes calculated.</p><p><strong>Figure S6.</strong> Effect of insecticides leafhopper population control. Only those with a number of applications higher or equal to five are presented. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.8488.</p><p><strong>Figure S7.</strong> Effect of insecticides on <i>Macrosteles quadrilineatus</i> and <i>Empoasca fabae</i> population control. All insecticides (n = 12) are represented but the statistical analysis was only performed with those that the number of applications was higher than 5. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.1781 for the aster leafhopper <i>M.</i> <i>quadrilineatus </i>and <i>p</i> = 0.6540 for the potato leafhopper <i>E. fabae</i>.</p><p><strong>Figure S8.</strong> Comparison among the Shannon index obtained for leafhopper populations in vineyards in 2007 and 2008 and for leafhopper populations in strawberry fields in 2021 and 2022 in Quebec.</p>
Supplementary Tables for Can leafhoppers help us trace the impact of climate change on agriculture?
<p>Supplementary Tables for the Preprint entitled: Can leafhoppers help us trace the impact of climate change on agriculture? to be posted in bioRxiv. </p> <p><strong>Table S1. </strong>Detailed information on the strawberry fields included in this study.</p> <p><strong>Table S2</strong>. Detailed information on the weather stations used to retrieve temperature and precipitation data used in this study </p> <p><strong>Table S3. </strong>Strawberry samples analyzed in this study with symptoms resembling strawberry green petal phytoplasma disease during both growing seasons studied here.</p> <p><strong>Table S4.</strong> The geographic location of all the strawberry green petal phytoplasma disease cases reported to the provincial laboratory in expertise in diagnostic and phytopathology in the last decade.</p> <p><strong>Table S5.</strong> Leafhopper species and the number of specimens per species analyzed by phytoplasma-specific PCR to detect the presence of the pathogen.</p> <p><strong>Table S6.</strong> Detailed information on the leafhoppers incubated with strawberry plants during the phytoplasma transmission assays.</p> <p><strong>Table S7.</strong> Detailed information on <em>Macosteles quadrilineatus</em> used to study the leafhopper microbiome.</p> <p><strong>Table S8. </strong>Detailed information on the insecticides used by strawberry growers during both grow seasons included in the study and those treatments selected for further statistic analyses.</p> <p><strong>Table S9. </strong>Identification and number of leafhopper species captured in strawberry fields in each geographic region screened in this study.</p> <p><strong>Table S10. </strong>Detailed information of diversity indexes Shannon and Simpson calculated using the data collected in this study.</p> <p><strong>Table S11.</strong> Fixed days and temperature values used during leafhopper populations modelling.</p> <p><strong>Table S12.</strong> Detailed information on the taxonomy of the phytoplasma strain SbGPQ affecting strawberry plants in eastern Canada by hybridization and illumine sequencing and by PCR amplification, cloning and Sanger sequencing.</p> <p><strong>Table S13.</strong> Detailed information on <em>Macosteles quadrilineatus</em> microbiome including OTUs, reads, and metadata information.</p> <p><strong>Table S14.</strong> Detailed information on the core microbiome for <em>Macosteles quadrilineatus</em> captured during each growing season and in common for all the leafhoppers analyzed during this study.</p> <p><strong>Table S15. </strong><span>BIC values for models selection. </span></p>
Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change
<p><span>Temperature has a large influence on insect abundances, thus under climate change, identifying major drivers affecting pest insect populations is critical to world food security and agricultural ecosystem health. Here, we conducted a meta-analysis with data obtained from 120 studies across China and Europe from 1970 to 2017 to reveal how climate and agricultural practices affect populations of wheat aphids. H</span><span>ere</span><span> we showed that aphid loads on wheat had distinct patterns between these two regions, with a significant increase in China but a decrease in Europe over this time period. Although temperature increased over this period in both regions, we found no evidence showing climate warming affected aphid loads. Rather, differences in pesticide use, fertilization, land use, and natural enemies between China and Europe may be key factors accounting for differences in aphid pest populations. These long-term data suggest that agricultural practices impact wheat aphid loads more than climate warming. </span></p>
Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change
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Data from: The impact of climate change on the potential distribution of agricultural pests: the case of the coffee white stem borer (Monochamus leuconotus P.) in Zimbabwe
The production of agricultural commodities faces increased risk of pests, diseases and other stresses due to climate change and variability. This study assesses the potential distribution of agricultural pests under projected climatic scenarios using evidence from the African coffee white stem borer (CWB), Monochamus leuconotus (Pascoe) (Coleoptera: Cerambycidae), an important pest of coffee in Zimbabwe. A species distribution modeling approach utilising Boosted Regression Trees (BRT) and Generalized Linear Models (GLM) was applied on current and projected climate data obtained from the WorldClim database and occurrence data (presence and absence) collected through on-farm biological surveys in Chipinge, Chimanimani, Mutare and Mutasa districts in Zimbabwe. Results from both the BRT and GLM indicate that precipitation-related variables are more important in determining species range for the CWB than temperature related variables. The CWB has extensive potential habitats in all coffee areas with Mutasa district having the largest model average area suitable for CWB under current and projected climatic conditions. Habitat ranges for CWB will increase under future climate scenarios for Chipinge, Chimanimani and Mutare districts while it will decrease in Mutasa district. The highest percentage change in area suitable for the CWB was for Chimanimani district with a model average of 49.1% (3 906 ha) increase in CWB range by 2080. The BRT and GLM predictions gave similar predicted ranges for Chipinge, Chimanimani and Mutasa districts compared to the high variation in current and projected habitat area for CWB in Mutare district. The study concludes that suitable area for CWB will increase significantly in Zimbabwe due to climate change and there is need to develop adaptation mechanisms.
Data from: Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?
Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability.
Data from: Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?
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Data from: The impact of climate change on the potential distribution of agricultural pests: the case of the coffee white stem borer (Monochamus leuconotus P.) in Zimbabwe
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Predicted impacts of climate change on wild and commercial berry habitats will have food security, conservation and agricultural implications
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