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9 results for “yield gap”
Crop-specific global fertilizer application rates from "Closing yield gaps through nutrient and water management"
<p>Crop-specific global maps of N, P2O5, and K2O fertilizer application rates circa the year 2000 from the following paper:</p> <p>Mueller, ND, JS Gerber, M Johnston, DK Ray, N Ramankutty, and JA Foley. 2012. Closing yield gaps through nutrient and water management. <em>Nature</em> <strong>490</strong>: 254–257</p> <p>Data are provided at five arc-minute resolution and are saved as netcdf files. Fertilizer application rates are estimated from reconciling various national and subnational data sources. See the Supplementary Information from the 2012 paper for a full description of data sources and methods. Data quality for each grid cell is described in a map layer. Files containing the text "totalcons" sum nutrient consumption across crops per grid cell, using crop harvested areas from Monfreda et al. 2008 Global Biogeochemical Cycles. For maize, wheat, and soybean N application rates, additional maps and csv files (containing the text "politboundaries") identify the political units around the world containing unique information. Crops and crop group categories are consistent with those utilized in Monfreda et al. 2008 Global Biogeochemical Cycles.</p>
Supplementary dataset: Phoamtonic designs yield sizeable 3D photonic band gaps
<p>Supplementary Dataset of the publication "<a href="http://www.pnas.org/content/116/47/23480">Phoamtonic designs yield sizeable 3D photonic band gaps</a>" by the same authors:</p> <p>M. A. Klatt, P. J. Steinhardt, S. Torquato. <em>Proc. Natl. Acad. Sci. U.S.A.</em> <strong>116</strong>:23480–23486 (2019)<br> <a href="https://doi.org/10.1073/pnas.1912730116">https://doi.org/10.1073/pnas.1912730116</a></p> <p>Database S1 "Gap maps": Raw data of the gap maps listing the gap--mid-gap ratios together with the corresponding rod radii, volume fractions, or dilectric contrast.</p> <p>Database S2 "Configurations": All triply-periodic nets and networks analyzed in this study. The readme file explains the file formats.</p> <p>Database S3 "Parameters and Data": All parameters for the photonic calculations, raw output and post-processed data sets.</p>
Global gridded maps of yield potential of the Global Yield Gap Atlas (GYGA)
<p>A complete description of maps' methods, accuracy, strengths, and limitations is available in <a href="https://doi.org/10.1038/s43016-024-01029-3">Aramburu-Merlos et al. (Nat. Food, 2024)</a>. </p> <p>Briefly, we combined site-specific yield potential estimates of the <a href="https://www.yieldgap.org/">Global Yield Gap Atlas</a> with gridded environmental predictors in a machine-learning metamodel to generate global maps of yield potential at a 30-arc-second resolution for maize, wheat, and rice, separately for irrigated and rainfed conditions. Model predictions were restricted to their <a href="https://doi.org/10.1111/2041-210X.13650">area of applicability</a> and lands harvested with the given crop and water regime condition (harvested area > 0.5% according to <a href="https://doi.org/10.7910/DVN/PRFF8V">SPAM v2.0</a>).</p> <p> </p>
Data from: Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems
Knowledge of production-system performance is largely based on observations at the experimental plot scale. Although yield gaps between plot-scale and field-scale research are widely acknowledged, their extent and persistence have not been experimentally examined in a systematic manner. At a site in southwest Michigan, we conducted a 6-y experiment to test the accuracy with which plot-scale crop-yield results can inform field-scale conclusions. We compared conventional versus alternative, that is, reduced-input and biologically based–organic, management practices for a corn–soybean–wheat rotation in a randomized complete block-design experiment, using 27 commercial-size agricultural fields. Nearby plot-scale experiments (0.02-ha to 1.0-ha plots) provided a comparison of plot versus field performance. We found that plot-scale yields well matched field-scale yields for conventional management but not for alternative systems. For all three crops, at the plot scale, reduced-input and conventional managements produced similar yields; at the field scale, reduced-input yields were lower than conventional. For soybeans at the plot scale, biological and conventional managements produced similar yields; at the field scale, biological yielded less than conventional. For corn, biological management produced lower yields than conventional in both plot- and field-scale experiments. Wheat yields appeared to be less affected by the experimental scale than corn and soybean. Conventional management was more resilient to field-scale challenges than alternative practices, which were more dependent on timely management interventions; in particular, mechanical weed control. Results underscore the need for much wider adoption of field-scale experimentation when assessing new technologies and production-system performance, especially as related to closing yield gaps in organic farming and in low-resourced systems typical of much of the developing world.
Data from: Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems
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Data from: Pollination mitigates cucumber yield gaps more than pesticide and fertilizer use in tropical smallholder gardens
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Data from: Diversification practices reduce organic to conventional yield gap
Agriculture today places great strains on biodiversity, soils, water and the atmosphere, and these strains will be exacerbated if current trends in population growth, meat and energy consumption, and food waste continue. Thus, farming systems that are both highly productive and minimize environmental harms are critically needed. How organic agriculture may contribute to world food production has been subject to vigorous debate over the past decade. Here, we revisit this topic comparing organic and conventional yields with a new meta-dataset three times larger than previously used (115 studies containing more than 1000 observations) and a new hierarchical analytical framework that can better account for the heterogeneity and structure in the data. We find organic yields are only 19.2% (±3.7%) lower than conventional yields, a smaller yield gap than previous estimates. More importantly, we find entirely different effects of crop types and management practices on the yield gap compared with previous studies. For example, we found no significant differences in yields for leguminous versus non-leguminous crops, perennials versus annuals or developed versus developing countries. Instead, we found the novel result that two agricultural diversification practices, multi-cropping and crop rotations, substantially reduce the yield gap (to 9 ± 4% and 8 ± 5%, respectively) when the methods were applied in only organic systems. These promising results, based on robust analysis of a larger meta-dataset, suggest that appropriate investment in agroecological research to improve organic management systems could greatly reduce or eliminate the yield gap for some crops or regions.
Schematic view of positive and negative yield gap with different types of soil management
<p>Schematic view of biomass production following different methods of crop production. This shows with imaginary numbers the possibilities of intercropping, and the importance of keeping intercropping or double cropping in mind when thinking of alternative agricultural models, as it could mean higher over all yieds.</p>
Data from: Diversification practices reduce organic to conventional yield gap
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